8.6.3.3 Medical Image Semantic Segmentation

Chapter Contents (Back)
Semantic Segmentation. Medical Images. General topic of medical image segmentation.

Cheng, K.S.[Kuo-Sheng], Lin, J.S.[Jzau-Sheng], Mao, C.W.[Chi-Wu],
The Application of Competitive Hopfield Neural Network to Medical Image Segmentation,
MedImg(15), No. 4, August 1996, pp. 560-567.
IEEE Top Reference. 0203
BibRef

Hansen, M.W.[Michael W.], Higgins, W.E.[William E.],
Relaxation Methods for Supervised Image Segmentation,
PAMI(19), No. 9, September 1997, pp. 949-962.
IEEE DOI 9710
BibRef
Earlier:
Watershed-driven relaxation labeling for image segmentation,
ICIP94(III: 460-464).
IEEE DOI 9411
Watershed driven relaxation labeling. Applied to 3D medical images. Use cues that indicate region shape. BibRef

Gal, Y.[Yaniv], Mehnert, A.[Andrew], Rose, S.[Stephen], Crozier, S.[Stuart],
Mutual information-based binarisation of multiple images of an object: An application in medical imaging,
IET-CV(7), No. 3, 2013, pp. 163-169.
DOI Link 1307
BibRef

Cardoso, M.J.[M. Jorge], Modat, M.[Marc], Wolz, R., Melbourne, A., Cash, D.[David], Rueckert, D., Ourselin, S.[Sebastien],
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion,
MedImg(34), No. 9, September 2015, pp. 1976-1988.
IEEE DOI 1509
Image segmentation Clinical annotations. BibRef

Korner, M.[Marco], Krishna, M.V.[Mahesh V.], Susse, H.[Herbert], Ortmann, W.[Wolfang], Denzler, J.[Joachim],
Regularized Geometric Hulls for Bio-medical Image Segmentation,
BMVA(2015), No. 4, 2015, pp. 1-12.
PDF File. 1509
BibRef

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.,
DRINet for Medical Image Segmentation,
MedImg(37), No. 11, November 2018, pp. 2453-2462.
IEEE DOI 1811
Image segmentation, Computer architecture, Convolution, Training, Medical diagnostic imaging, Standards, abdominal organ segmentation BibRef

Bi, L.[Lei], Feng, D.D.[David Dagan], Kim, J.M.[Jin-Man],
Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation,
VC(34), No. 6-8, June 2018, pp. 1043-1052.
WWW Link. 1806
BibRef

Schipaanboord, B., Boukerroui, D., Peressutti, D., van Soest, J., Lustberg, T., Kadir, T., Dekker, A., van Elmpt, W., Gooding, M.,
Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory,
MedImg(38), No. 1, January 2019, pp. 99-106.
IEEE DOI 1901
Image segmentation, Databases, Computed tomography, Planning, Head, Neck, Tumors, Radiotherapy, extreme value theory, auto-contouring BibRef

Lu, L., Harrison, A.P.,
Deep Medical Image Computing in Preventive and Precision Medicine,
MultMedMag(25), No. 3, July 2018, pp. 109-113.
IEEE DOI 1901
Biomedical imaging, Tumors, Computed tomography, Image segmentation, Biomarkers BibRef

Karimi, D., Salcudean, S.E.,
Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks,
MedImg(39), No. 2, February 2020, pp. 499-513.
IEEE DOI 2002
Image segmentation, Biomedical imaging, Training, Sensitivity, convolutional neural networks BibRef

Lin, D.Y.[Dong-Yun], Li, Y.Q.[Yi-Qun], Nwe, T.L.[Tin Lay], Dong, S.[Sheng], Oo, Z.M.[Zaw Min],
RefineU-Net: Improved U-Net with progressive global feedbacks and residual attention guided local refinement for medical image segmentation,
PRL(138), 2020, pp. 267-275.
Elsevier DOI 2010
U-Net, Medical image segmentation, Progressive global feedbacks, Local refinement, Residual attention gate BibRef

Mehrtash, A., Wells, W.M., Tempany, C.M., Abolmaesumi, P., Kapur, T.,
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation,
MedImg(39), No. 12, December 2020, pp. 3868-3878.
IEEE DOI 2012
Uncertainty, Image segmentation, Calibration, Estimation, Biomedical imaging, Artificial neural networks, Bayes methods, fully convolutional neural networks BibRef

Huang, S.Q.[Shao-Qiong], Huang, M.X.[Meng-Xing], Zhang, Y.[Yu], Chen, J.[Jing], Bhatti, U.[Uzair],
Medical Image Segmentation Using Deep Learning with Feature Enhancement,
IET-IPR(14), No. 14, December 2020, pp. 3324-3332.
DOI Link 2012
BibRef

Eelbode, T., Bertels, J., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., Blaschko, M.B.,
Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index,
MedImg(39), No. 11, November 2020, pp. 3679-3690.
IEEE DOI 2011
Indexes, Image segmentation, Biomedical imaging, Measurement, Risk management, Training, Task analysis, Dice, Jaccard, Tversky BibRef

Ren, X., Ahmad, S., Zhang, L., Xiang, L., Nie, D., Yang, F., Wang, Q., Shen, D.,
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation,
IP(29), 2020, pp. 7497-7510.
IEEE DOI 2007
Semantic segmentation, fully convolutional network, task decomposition, sync-regularization, deep learning BibRef

Wang, D.[Dan], Hu, G.Q.[Guo-Qing], Lyu, C.Z.[Cheng-Zhi],
Multi-path connected network for medical image segmentation,
JVCIR(71), 2020, pp. 102852.
Elsevier DOI 2009
Medical image segmentation, Multi-path connections, Convolutional neural networks, Encoder-decoder structure BibRef

Kim, B.N.[Bach Ngoc], Dolz, J.[Jose], Jodoin, P.M.[Pierre-Marc], Desrosiers, C.[Christian],
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images,
MedImg(40), No. 7, July 2021, pp. 1737-1749.
IEEE DOI 2107
Image segmentation, Task analysis, Biomedical imaging, Servers, Training, Privacy, Image analysis, Adversarial, deep learning, segmentation BibRef

Huang, H.M.[Hui-Min], Zheng, H.[Han], Lin, L.F.[Lan-Fen], Cai, M.[Ming], Hu, H.J.[Hong-Jie], Zhang, Q.W.[Qiao-Wei], Chen, Q.Q.[Qing-Qing], Iwamoto, Y.[Yutaro], Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Tong, R.F.[Ruo-Feng],
Medical Image Segmentation With Deep Atlas Prior,
MedImg(40), No. 12, December 2021, pp. 3519-3530.
IEEE DOI 2112
Image segmentation, Bayes methods, Probabilistic logic, Deep learning, Adaptation models, Task analysis, adaptive bayesian loss BibRef

El Jurdi, R.[Rosana], Petitjean, C.[Caroline], Honeine, P.[Paul], Cheplygina, V.[Veronika], Abdallah, F.[Fahed],
High-level prior-based loss functions for medical image segmentation: A survey,
CVIU(210), 2021, pp. 103248.
Elsevier DOI 2109
Survey, Segmentation. Survey, Medical. Prior-based loss functions, Anatomical constraint losses, Convolutional neural networks, Medical image segmentation, Deep learning BibRef

Yu, Q.[Qian], Gao, Y.[Yang], Zheng, Y.F.[Ye-Feng], Zhu, J.B.[Jian-Bing], Dai, Y.K.[Ya-Kang], Shi, Y.H.[Ying-Huan],
Crossover-Net: Leveraging vertical-horizontal crossover relation for robust medical image segmentation,
PR(113), 2021, pp. 107756.
Elsevier DOI 2103
Code, Segmentation.
WWW Link. Convolutional neural network, Non-elongated tissue, Crossover-Net, Image segmentation, Crossover-patch BibRef

Nath, V.[Vishwesh], Yang, D.[Dong], Landman, B.A.[Bennett A.], Xu, D.G.[Da-Guang], Roth, H.R.[Holger R.],
Diminishing Uncertainty Within the Training Pool: Active Learning for Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2534-2547.
IEEE DOI 2110
Image segmentation, Biomedical imaging, Deep learning, Training, Task analysis, Mutual information, Hippocampus, Deep Learning, SVGD BibRef

Feng, R.[Ruiwei], Zheng, X.S.[Xiang-Shang], Gao, T.X.[Tian-Xiang], Chen, J.[Jintai], Wang, W.Z.[Wen-Zhe], Chen, D.Z.[Danny Z.], Wu, J.[Jian],
Interactive Few-Shot Learning: Limited Supervision, Better Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2575-2588.
IEEE DOI 2110
Image segmentation, Task analysis, Biomedical imaging, Training, Annotations, Deep learning, Optimization, limited supervision BibRef

Cui, H.J.[Heng-Ji], Wei, D.[Dong], Ma, K.[Kai], Gu, S.[Shi], Zheng, Y.F.[Ye-Feng],
A Unified Framework for Generalized Low-Shot Medical Image Segmentation With Scarce Data,
MedImg(40), No. 10, October 2021, pp. 2656-2671.
IEEE DOI 2110
Image segmentation, Biomedical imaging, Training, Annotations, Task analysis, Diseases, adaptive mixing coefficients BibRef

Wang, L.[Lu], Guo, D.[Dong], Wang, G.[Guotai], Zhang, S.T.[Shao-Ting],
Annotation-Efficient Learning for Medical Image Segmentation Based on Noisy Pseudo Labels and Adversarial Learning,
MedImg(40), No. 10, October 2021, pp. 2795-2807.
IEEE DOI 2110
Image segmentation, Training, Annotations, Shape, Biomedical imaging, Noise measurement, Deep learning, Segmentation, deep learning, noisy labels BibRef

Qu, L.[Lei], Wang, M.[Meng], Guo, K.X.[Kai-Xuan], Wan, W.[Wan], Liu, Y.[Yu], Tang, J.[Jun], Wu, J.[Jun], Duan, P.[Peng],
Biomedical image segmentation based on full-Resolution network,
PRL(153), 2022, pp. 232-238.
Elsevier DOI 2201
Image Segmentation, Biomedical Image, Full-resolution, Convolutional Neural Network BibRef

Wang, R.S.[Ri-Sheng], Lei, T.[Tao], Cui, R.X.[Rui-Xia], Zhang, B.T.[Bing-Tao], Meng, H.Y.[Hong-Ying], Nandi, A.K.[Asoke K.],
Medical image segmentation using deep learning: A survey,
IET-IPR(16), No. 5, 2022, pp. 1243-1267.
DOI Link 2203
Survey, Medical Images. BibRef

Minaee, S.[Shervin], Boykov, Y.Y.[Yuri Y.], Porikli, F.M.[Fatih M.], Plaza, A.[Antonio], Kehtarnavaz, N.[Nasser], Terzopoulos, D.[Demetri],
Image Segmentation Using Deep Learning: A Survey,
PAMI(44), No. 7, July 2022, pp. 3523-3542.
IEEE DOI 2206
Image segmentation, Computer architecture, Semantics, Deep learning, Computational modeling, medical image segmentation BibRef

Zhang, Z.X.[Zhen-Xi], Tian, C.[Chunna], Gao, X.B.[Xin-Bo], Li, J.[Jie], Jiao, Z.C.[Zhi-Cheng], Wang, C.[Cui], Zhong, Z.[Zhusi],
Collaborative boundary-aware context encoding networks for error map prediction,
PR(125), 2022, pp. 108515.
Elsevier DOI 2203
Segmentation quality assessment, Error map prediction, Medical image segmentation BibRef

Shi, Y.H.[Ying-Huan], Zhang, J.[Jian], Ling, T.[Tong], Lu, J.W.[Ji-Wen], Zheng, Y.F.[Ye-Feng], Yu, Q.[Qian], Qi, L.[Lei], Gao, Y.[Yang],
Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation,
MedImg(41), No. 3, March 2022, pp. 608-620.
IEEE DOI 2203
Uncertainty, Image segmentation, Estimation, Training, Biomedical imaging, Computational modeling, Task analysis, conservative-radical networks BibRef

Gut, D.[Daniel], Tabor, Z.[Zbislaw], Szymkowski, M.[Mateusz], Rozynek, M.[Milosz], Kucybala, I.[Iwona], Wojciechowski, W.[Wadim],
Benchmarking of Deep Architectures for Segmentation of Medical Images,
MedImg(41), No. 11, November 2022, pp. 3231-3241.
IEEE DOI 2211
Image segmentation, Task analysis, Training, Biomedical imaging, Computed tomography, Magnetic resonance imaging, Benchmark, segmentation BibRef

Zhang, Y.C.[Yun-Chu], Dong, J.F.[Jian-Fei],
2K-Fold-Net and feature enhanced 4-Fold-Net for medical image segmentation,
PR(127), 2022, pp. 108625.
Elsevier DOI 2205
2K-Fold-Net, EF-Net, U-Net, AFE, Image segmentation BibRef

Wang, K.[Kun], Zhang, X.H.[Xiao-Hong], Zhang, X.B.[Xiang-Bo], Lu, Y.T.[Yu-Ting], Huang, S.[Sheng], Yang, D.[Dan],
EANet: Iterative edge attention network for medical image segmentation,
PR(127), 2022, pp. 108636.
Elsevier DOI 2205
Medical image segmentation, Dynamic scale-aware context, Edge attention preservation, Multi-level pairwise regression, Computer-aided diagnosis (CAD) BibRef

Liu, Z.H.[Zi-Hao], Li, Z.W.[Zhuo-Wei], Hu, Z.Q.[Zhi-Qiang], Xia, Q.[Qing], Xiong, R.Q.[Rui-Qin], Zhang, S.T.[Shao-Ting], Jiang, T.T.[Ting-Ting],
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation,
MedImg(41), No. 11, November 2022, pp. 3398-3410.
IEEE DOI 2211
Code, Segmentation.
WWW Link. Uncertainty, Image segmentation, Training, Task analysis, Medical diagnostic imaging, Decoding, neural network BibRef

Song, J.H.[Jia-Huan], Chen, X.J.[Xin-Jian], Zhu, Q.[Qianlong], Shi, F.[Fei], Xiang, D.[Dehui], Chen, Z.Y.[Zhong-Yue], Fan, Y.[Ying], Pan, L.J.[Ling-Jiao], Zhu, W.F.[Wei-Fang],
Global and Local Feature Reconstruction for Medical Image Segmentation,
MedImg(41), No. 9, September 2022, pp. 2273-2284.
IEEE DOI 2209
Feature extraction, Image reconstruction, Semantics, Image segmentation, Convolution, Biomedical imaging, Task analysis, local feature reconstruction module BibRef

Nan, Y.[Yang], Tang, P.[Peng], Zhang, G.[Guyue], Zeng, C.H.[Cai-Hong], Liu, Z.H.[Zhi-Hong], Gao, Z.[Zhifan], Zhang, H.[Heye], Yang, G.[Guang],
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network,
MedImg(41), No. 12, December 2022, pp. 3799-3811.
IEEE DOI 2212
Image segmentation, Pathology, Training, Manuals, Annotations, Deep learning, Unsupervised learning, Semantic segmentation, tissue segmentation BibRef

Zheng, R.F.[Rui-Feng], Zhong, Y.[Ying], Yan, S.[Senxiang], Sun, H.C.[Hong-Cheng], Shen, H.B.[Hai-Bin], Huang, K.[Kejie],
MsVRL: Self-Supervised Multiscale Visual Representation Learning via Cross-Level Consistency for Medical Image Segmentation,
MedImg(42), No. 1, January 2023, pp. 91-102.
IEEE DOI 2301
Image segmentation, Task analysis, Visualization, Self-supervised learning, Medical diagnostic imaging, abdomen BibRef

Hu, S.S.[Shi-Shuai], Liao, Z.[Zehui], Zhang, J.P.[Jian-Peng], Xia, Y.[Yong],
Domain and Content Adaptive Convolution Based Multi-Source Domain Generalization for Medical Image Segmentation,
MedImg(42), No. 1, January 2023, pp. 233-244.
IEEE DOI 2301
Image segmentation, Adaptation models, Head, Biomedical imaging, Convolution, Training, Data models, Domain generalization, deep learning BibRef

Valverde, J.M.[Juan Miguel], Tohka, J.[Jussi],
Region-wise loss for biomedical image segmentation,
PR(136), 2023, pp. 109208.
Elsevier DOI 2301
Deep learning, Segmentation, Medical imaging, Loss function BibRef

Yuan, F.N.[Fei-Niu], Zhang, Z.X.[Zheng-Xiao], Fang, Z.J.[Zhi-Jun],
An effective CNN and Transformer complementary network for medical image segmentation,
PR(136), 2023, pp. 109228.
Elsevier DOI 2301
Transformer, Medical image segmentation, Feature complementary module, Cross-domain fusion, Convolutional Neural Network BibRef

Li, S.[Shumeng], Cai, H.[Heng], Qi, L.[Lei], Yu, Q.[Qian], Shi, Y.[Yinghuan], Gao, Y.[Yang],
PLN: Parasitic-Like Network for Barely Supervised Medical Image Segmentation,
MedImg(42), No. 3, March 2023, pp. 582-593.
IEEE DOI 2303
Annotations, Image segmentation, Training, Biomedical imaging, Task analysis, Shape, 3D medical image segmentation, parasitic-like network BibRef

Cai, H.[Heng], Li, S.[Shumeng], Qi, L.[Lei], Yu, Q.[Qian], Shi, Y.[Yinghuan], Gao, Y.[Yang],
Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation,
CVPR23(3302-3311)
IEEE DOI 2309
BibRef

Zou, W.X.[Wen-Xuan], Qi, X.Q.[Xing-Qun], Zhou, W.T.[Wan-Ting], Sun, M.[Muyi], Sun, Z.A.[Zhen-An], Shan, C.F.[Cai-Feng],
Graph Flow: Cross-Layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation,
MedImg(42), No. 4, April 2023, pp. 1159-1171.
IEEE DOI 2304
Image segmentation, Knowledge engineering, Medical diagnostic imaging, Sun, Cross layer design, Annotations, graph flow BibRef

Yuan, C.[Chao], Wang, Y.B.[Yan-Bo], Xiao, Y.[Yunxuan],
LSUnetMix: Fuse channel feature information with long-short term memory,
IET-CV(17), No. 2, 2023, pp. 241-249.
DOI Link 2304
biomedical engineering, image segmentation, learning (artificial intelligence) BibRef

Wang, K.[Kun], Zhang, X.H.[Xiao-Hong], Lu, Y.T.[Yu-Ting], Zhang, W.[Wei], Huang, S.[Sheng], Yang, D.[Dan],
GSAL: Geometric structure adversarial learning for robust medical image segmentation,
PR(140), 2023, pp. 109596.
Elsevier DOI 2305
Medical image segmentation, Geometric structure learning, Adversarial learning, Computer-Aided diagnosis (CAD) BibRef

Li, H.[He], Iwamoto, Y.[Yutaro], Han, X.H.[Xian-Hua], Lin, L.F.[Lan-Fen], Furukawa, A.[Akira], Kanasaki, S.[Shuzo], Chen, Y.W.[Yen-Wei],
3D Multiple-Contextual ROI-Attention Network for Efficient and Accurate Volumetric Medical Image Segmentation,
IEICE(E106-D), No. 5, May 2023, pp. 1027-1037.
WWW Link. 2305
BibRef

Fang, W.H.[Wen-Hao], Han, X.H.[Xian-Hua],
Spatial and Channel Attention Modulated Network for Medical Image Segmentation,
MLCSA20(3-17).
Springer DOI 2103
BibRef

Lei, T.[Tao], Zhang, D.[Dong], Du, X.G.[Xiao-Gang], Wang, X.[Xuan], Wan, Y.[Yong], Nandi, A.K.[Asoke K.],
Semi-Supervised Medical Image Segmentation Using Adversarial Consistency Learning and Dynamic Convolution Network,
MedImg(42), No. 5, May 2023, pp. 1265-1277.
IEEE DOI 2305
Image segmentation, Training, Data models, Perturbation methods, Medical diagnostic imaging, Convolution, adversarial learning BibRef

Song, Y.Y.[You-Yi], Yu, L.[Lequan], Lei, B.Y.[Bai-Ying], Choi, K.S.[Kup-Sze], Qin, J.[Jing],
Data Discernment for Affordable Training in Medical Image Segmentation,
MedImg(42), No. 5, May 2023, pp. 1431-1445.
IEEE DOI 2305
Training, Image segmentation, Biomedical imaging, Task analysis, Training data, Programming, Annotations, Data discernment, medical image segmentation BibRef

Huang, X.H.[Xiao-Hong], Deng, Z.F.[Zhi-Fang], Li, D.D.[Dan-Dan], Yuan, X.G.[Xue-Guang], Fu, Y.[Ying],
MISSFormer: An Effective Transformer for 2D Medical Image Segmentation,
MedImg(42), No. 5, May 2023, pp. 1484-1494.
IEEE DOI 2305
Transformers, Image segmentation, Task analysis, Bridges, Medical diagnostic imaging, Feature extraction, Merging, segmentation BibRef

Hao, D.[Dechen], Li, H.L.[Hua-Ling],
A graph-based edge attention gate medical image segmentation method,
IET-IPR(17), No. 7, 2023, pp. 2142-2157.
DOI Link 2305
dropout residual graph convolution block, edge attention gate, medical image segmentation, UNet++, weighted loss function BibRef

Li, Z.[Zeju], Kamnitsas, K.[Konstantinos], Ouyang, C.[Cheng], Chen, C.[Chen], Glocker, B.[Ben],
Context Label Learning: Improving Background Class Representations in Semantic Segmentation,
MedImg(42), No. 6, June 2023, pp. 1885-1896.
IEEE DOI 2306
Image segmentation, Tumors, Task analysis, Liver, Kidney, Context modeling, Training, Underfitting, multi-task learning, image segmentation BibRef

Fang, C.W.[Chao-Wei], Wang, Q.[Qian], Cheng, L.[Lechao], Gao, Z.F.[Zhi-Fan], Pan, C.W.[Cheng-Wei], Cao, Z.[Zhen], Zheng, Z.H.[Zhao-Hui], Zhang, D.W.[Ding-Wen],
Reliable Mutual Distillation for Medical Image Segmentation Under Imperfect Annotations,
MedImg(42), No. 6, June 2023, pp. 1720-1734.
IEEE DOI 2306
Image segmentation, Reliability, Annotations, Noise measurement, Data models, Training, Cleaning, Imperfect annotation, medical image segmentation BibRef

Xian, J.L.[Jun-Lin], Li, X.[Xiang], Tu, D.D.[Dan-Dan], Zhu, S.[Senhua], Zhang, C.Z.[Chang-Zheng], Liu, X.W.[Xiao-Wu], Li, X.[Xin], Yang, X.[Xin],
Unsupervised Cross-Modality Adaptation via Dual Structural-Oriented Guidance for 3D Medical Image Segmentation,
MedImg(42), No. 6, June 2023, pp. 1774-1785.
IEEE DOI 2306
Image segmentation, Biomedical imaging, Feature extraction, Training, Magnetic resonance imaging, Computed tomography, structural-oriented guidance BibRef

Shu, Y.C.[Yu-Cheng], Li, H.[Hengbo], Xiao, B.[Bin], Bi, X.L.[Xiu-Li], Li, W.S.[Wei-Sheng],
Cross-Mix Monitoring for Medical Image Segmentation With Limited Supervision,
MultMed(25), 2023, pp. 1700-1712.
IEEE DOI 2306
Image segmentation, Biomedical imaging, Training, Data models, Perturbation methods, Task analysis, Monitoring, transductive monitor BibRef

Ta, N.[Na], Chen, H.P.[Hai-Peng], Lyu, Y.D.[Ying-Da], Wang, X.[Xue], Shi, Z.[Zenan], Liu, Z.H.[Zhe-Hao],
A complementary and contrastive network for stimulus segmentation and generalization,
IVC(135), 2023, pp. 104694.
Elsevier DOI 2306
Complementary feature, Contrastive feature, Mutual attention, Synthetic data augmentation, Medical image segmentation BibRef

Wicaksana, J.[Jeffry], Yan, Z.Q.[Zeng-Qiang], Zhang, D.[Dong], Huang, X.J.[Xi-Jie], Wu, H.M.[Hui-Min], Yang, X.[Xin], Cheng, K.T.[Kwang-Ting],
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation,
MedImg(42), No. 7, July 2023, pp. 1955-1968.
IEEE DOI 2307
Image segmentation, Data models, Biomedical imaging, Training, Federated learning, Lesions, Adaptation models, Federated learning, adaptive weight aggregation BibRef

Jiang, M.[Meirui], Yang, H.Z.[Hong-Zheng], Cheng, C.[Chen], Dou, Q.[Qi],
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation,
MedImg(42), No. 7, July 2023, pp. 2106-2117.
IEEE DOI 2307
Adaptation models, Data models, Training, Routing, Image segmentation, Biomedical imaging, Task analysis, data heterogeneity BibRef

Gao, Z.[Zheyao], Wu, F.[Fuping], Gao, W.G.[Wei-Guo], Zhuang, X.[Xiahai],
A New Framework of Swarm Learning Consolidating Knowledge From Multi-Center Non-IID Data for Medical Image Segmentation,
MedImg(42), No. 7, July 2023, pp. 2118-2129.
IEEE DOI 2307
Image segmentation, Training, Data models, Task analysis, Biomedical imaging, Distributed databases, Distance learning, swarm learning BibRef

Lin, X.[Xian], Yu, L.[Li], Cheng, K.T.[Kwang-Ting], Yan, Z.Q.[Zeng-Qiang],
The Lighter the Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning,
MedImg(42), No. 8, August 2023, pp. 2325-2337.
IEEE DOI 2308
Transformers, Biomedical imaging, Image segmentation, Training, Task analysis, Computational complexity, Costs, Adaptive pruning, transformer BibRef

Huang, S.Q.[Shi-Qi], Xu, T.F.[Ting-Fa], Shen, N.[Ning], Mu, F.[Feng], Li, J.A.[Jian-An],
Rethinking Few-Shot Medical Segmentation: A Vector Quantization View,
CVPR23(3072-3081)
IEEE DOI 2309
BibRef

Chen, Y.Z.[Yi-Zheng], Yu, L.[Lequan], Wang, J.Y.[Jen-Yeu], Panjwani, N.[Neil], Obeid, J.P.[Jean-Pierre], Liu, W.[Wu], Liu, L.[Lianli], Kovalchuk, N.[Nataliya], Gensheimer, M.F.[Michael Francis], Vitzthum, L.K.[Lucas Kas], Beadle, B.M.[Beth M.], Chang, D.T.[Daniel T.], Le, Q.T.[Quynh-Thu], Han, B.[Bin], Xing, L.[Lei],
Adaptive Region-Specific Loss for Improved Medical Image Segmentation,
PAMI(45), No. 11, November 2023, pp. 13408-13421.
IEEE DOI 2310
BibRef

He, A.[Along], Wang, K.[Kai], Li, T.[Tao], Du, C.[Chengkun], Xia, S.[Shuang], Fu, H.Z.[Hua-Zhu],
H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation,
MedImg(42), No. 9, September 2023, pp. 2763-2775.
IEEE DOI 2310
BibRef

Wang, N.[Nan], Lin, S.H.[Shao-Hui], Li, X.X.[Xiao-Xiao], Li, K.[Ke], Shen, Y.[Yunhang], Gao, Y.[Yue], Ma, L.Z.[Li-Zhuang],
MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet,
MedImg(42), No. 9, September 2023, pp. 2740-2750.
IEEE DOI 2310
BibRef

Du, H.[Hao], Dong, Q.H.[Qi-Hua], Xu, Y.[Yan], Liao, J.[Jing],
Weakly-Supervised 3D Medical Image Segmentation Using Geometric Prior and Contrastive Similarity,
MedImg(42), No. 10, October 2023, pp. 2936-2947.
IEEE DOI 2310
BibRef

Zhang, J.Y.[Jing-Yang], Gu, R.[Ran], Xue, P.[Peng], Liu, M.X.[Mian-Xin], Zheng, H.[Hao], Zheng, Y.F.[Ye-Feng], Ma, L.[Lei], Wang, G.[Guotai], Gu, L.[Lixu],
S3R: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites,
MedImg(42), No. 9, September 2023, pp. 2539-2551.
IEEE DOI 2310
BibRef

Xu, M.C.[Mou-Cheng], Zhou, Y.K.[Yu-Kun], Jin, C.[Chen], de Groot, M.[Marius], Alexander, D.C.[Daniel C.], Oxtoby, N.P.[Neil P.], Jacob, J.[Joseph],
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations With Limited Labels,
MedImg(42), No. 10, October 2023, pp. 2988-2999.
IEEE DOI 2310
BibRef

Wang, S.S.[Sheng-Sheng], Fu, Z.[Zihao], Wang, B.[Bilin], Hu, Y.L.[Yu-Long],
Fusing feature and output space for unsupervised domain adaptation on medical image segmentation,
IJIST(33), No. 5, 2023, pp. 1672-1681.
DOI Link 2310
adversarial domain adaptation, domain adaptation, image segmentation, medical image BibRef

Pang, S.C.[Shu-Chao], Du, A.[Anan], Orgun, M.A.[Mehmet A.], Wang, Y.[Yan], Sheng, Q.Z.[Quan Z.], Wang, S.J.[Shou-Jin], Huang, X.S.[Xiao-Shui], Yu, Z.M.[Zhen-Mei],
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation,
Cyber(53), No. 11, November 2023, pp. 6776-6787.
IEEE DOI 2310
BibRef

Liu, S.[Shidi], Liu, C.P.[Chun-Ping], Ji, Y.[Yi], Li, Y.[Ying],
Regional Consistency for Semi-Supervised Segmentation of 3D Medical Images,
SPLetters(30), 2023, pp. 1307-1311.
IEEE DOI 2310
BibRef

Wu, H.M.[Hui-Min], Li, X.M.[Xiao-Meng], Lin, Y.Q.[Yi-Qun], Cheng, K.T.[Kwang-Ting],
Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation,
MedImg(42), No. 11, November 2023, pp. 3244-3255.
IEEE DOI Code:
WWW Link. 2311
BibRef

Li, Z.[Zeju], Kamnitsas, K.[Konstantinos], Dou, Q.[Qi], Qin, C.[Chen], Glocker, B.[Ben],
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation,
MedImg(42), No. 11, November 2023, pp. 3323-3335.
IEEE DOI Code:
WWW Link. 2311
BibRef

Ji, Q.L.[Qiu-Lang], Wang, J.H.[Ji-Hong], Ding, C.[Caifu], Wang, Y.H.[Yu-Hang], Zhou, W.[Wen], Liu, Z.J.[Zi-Jie], Yang, C.[Chen],
DMAGNet: Dual-path multi-scale attention guided network for medical image segmentation,
IET-IPR(17), No. 13, 2023, pp. 3631-3644.
DOI Link 2311
codecs, convolutional neural nets, image processing, image segmentation BibRef

Tang, C.[Cheng], Zeng, X.[Xinyi], Zhou, L.P.[Lu-Ping], Zhou, Q.Z.[Qi-Zheng], Wang, P.[Peng], Wu, X.[Xi], Ren, H.P.[Hong-Ping], Zhou, J.[Jiliu], Wang, Y.[Yan],
Semi-supervised medical image segmentation via hard positives oriented contrastive learning,
PR(146), 2024, pp. 110020.
Elsevier DOI Code:
WWW Link. 2311
Hard positives, Contrastive learning, Semi-supervised learning, Medical image segmentation BibRef

Chen, J.K.[Jing-Kun], Chen, C.R.[Chang-Rui], Huang, W.J.[Wen-Jian], Zhang, J.G.[Jian-Guo], Debattista, K.[Kurt], Han, J.G.[Jun-Gong],
Dynamic contrastive learning guided by class confidence and confusion degree for medical image segmentation,
PR(145), 2024, pp. 109881.
Elsevier DOI 2311
Class confusion degree, Dynamic contrastive learning, Medical image segmentation BibRef

Qiu, Z.X.[Zhong-Xi], Hu, Y.[Yan], Chen, X.S.[Xiao-Shan], Zeng, D.[Dan], Hu, Q.Y.[Qing-Yong], Liu, J.[Jiang],
Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image Segmentation,
PAMI(46), No. 1, January 2024, pp. 451-464.
IEEE DOI 2312
BibRef

Zhang, J.J.[Jiao-Jiao], Zhang, S.[Shuo], Shen, X.Q.[Xiao-Qian], Lukasiewicz, T.[Thomas], Xu, Z.H.[Zheng-Hua],
Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative Adversarial Networks for Self-Supervised Medical Image Segmentation,
MedImg(43), No. 1, January 2024, pp. 76-95.
IEEE DOI 2401
BibRef

Lei, W.H.[Wen-Hui], Su, Q.[Qi], Jiang, T.Y.[Tian-Yu], Gu, R.[Ran], Wang, N.[Na], Liu, X.L.[Xing-Long], Wang, G.[Guotai], Zhang, X.F.[Xiao-Fan], Zhang, S.T.[Shao-Ting],
One-Shot Weakly-Supervised Segmentation in 3D Medical Images,
MedImg(43), No. 1, January 2024, pp. 175-189.
IEEE DOI Code:
WWW Link. 2401
BibRef

Wang, J.C.[Jia-Cheng], Jin, Y.M.[Yue-Ming], Stoyanov, D.[Danail], Wang, L.S.[Lian-Sheng],
FedDP: Dual Personalization in Federated Medical Image Segmentation,
MedImg(43), No. 1, January 2024, pp. 297-308.
IEEE DOI Code:
WWW Link. 2401
BibRef
Earlier: A1, A2, A4, Only:
Personalizing Federated Medical Image Segmentation via Local Calibration,
ECCV22(XXI:456-472).
Springer DOI 2211
BibRef

Tong, S.S.[Shan-Shan], Zuo, Z.T.[Zhen-Tao], Liu, Z.[Zuxiang], Sun, D.[Dengdi], Zhou, T.G.[Tian-Gang],
Hybrid attention mechanism of feature fusion for medical image segmentation,
IET-IPR(18), No. 1, 2024, pp. 77-87.
DOI Link 2401
biomedical imaging, computer vision, image segmentation, medical image processing BibRef

Lu, C.Z.[Cheng-Zhun], Xia, Z.R.[Zhang-Run], Przystupa, K.[Krzysztof], Kochan, O.[Orest], Su, J.[Jun],
DCELANM-Net: Medical image segmentation based on dual channel efficient layer aggregation network with learner,
IJIST(34), No. 1, 2024, pp. e22960.
DOI Link 2401
CNN, medical image segmentation, self-supervised learning, transformer BibRef

Zhao, Y.Y.[Yi-Yang], Li, J.[Jinjiang], Liu, Y.[Yepeng],
Dynamic weight HiLo attention network for medical image multiple organ segmentation,
IJIST(34), No. 1, 2024, pp. e22966.
DOI Link 2401
attention mechanism, convolutional neural networks, medical image segmentation, multiscale feature extraction BibRef

Ming, Q.[Qi], Xiao, X.W.[Xiao-Wu],
Towards Accurate Medical Image Segmentation with Gradient-Optimized Dice Loss,
SPLetters(31), 2024, pp. 191-195.
IEEE DOI 2401
BibRef


Schmidt, A.[Arne], Morales-┴lvarez, P.[Pablo], Molina, R.[Rafael],
Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation,
ICCV23(21040-21049)
IEEE DOI 2401
BibRef

Singh, P.[Pranav], Chen, L.[Luoyao], Chen, M.[Mei], Pan, J.Q.[Jin-Qian], Chukkapalli, R.[Raviteja], Chaudhari, S.[Shravan], Cirrone, J.[Jacopo],
Enhancing Medical Image Segmentation: Optimizing Cross-Entropy Weights and Post-Processing with Autoencoders,
CVAMD23(2676-2685)
IEEE DOI 2401
BibRef

Pandey, S.[Sumit], Chen, K.F.[Kuan-Fu], Dam, E.B.[Erik B.],
Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models,
CVAMD23(2584-2590)
IEEE DOI 2401
BibRef

Pal, D.[Debojyoti], Meena, T.[Tanushree], Mahapatra, D.[Dwarikanath], Roy, S.[Sudipta],
AW-Net: A Novel Fully Connected Attention-based Medical Image Segmentation Model,
CVAMD23(2524-2533)
IEEE DOI Code:
WWW Link. 2401
BibRef

Bastico, M.[Matteo], Ryckelynck, D.[David], CortÚ, L.[Laurent], Tillier, Y.[Yannick], DecenciŔre, E.[Etienne],
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers,
LXCV-ICCV23(4130-4140)
IEEE DOI Code:
WWW Link. 2401
BibRef

Butoi, V.I.[Victor Ion], Ortiz, J.J.G.[Jose Javier Gonzalez], Ma, T.Y.[Tian-Yu], Sabuncu, M.R.[Mert R.], Guttag, J.[John], Dalca, A.V.[Adrian V.],
UniverSeg: Universal Medical Image Segmentation,
ICCV23(21381-21394)
IEEE DOI Code:
WWW Link. 2401
BibRef

Marinov, Z.[Zdravko], Rei▀, S.[Simon], Kersting, D.[David], Kleesiek, J.[Jens], Stiefelhagen, R.[Rainer],
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging,
CVAMD23(2275-2285)
IEEE DOI Code:
WWW Link. 2401
BibRef

Hu, H.[Haigen], Jin, Z.C.[Zhi-Chao], Zhou, Q.W.[Qian-Wei], Guan, Q.[Qiu], Chen, Q.[Qi],
CTI-Unet: Hybrid Local Features and Global Representations Efficiently,
ICIP23(735-739)
IEEE DOI Code:
WWW Link. 2312
BibRef

Li, J.Q.[Jiu-Qiang],
MCTE: Marrying Convolution and Transformer Efficiently for End-to-End Medical Image Segmentation,
ICIP23(1100-1104)
IEEE DOI 2312
BibRef

Huang, Y.[Yao], Liu, J.M.[Jian-Ming], Chen, H.[Hua],
Self-Reinforcing For Few-Shot Medical Image Segmentation,
ICIP23(655-659)
IEEE DOI Code:
WWW Link. 2312
BibRef

Jiang, M.R.[Mei-Rui], Roth, H.R.[Holger R.], Li, W.Q.[Wen-Qi], Yang, D.[Dong], Zhao, C.[Can], Nath, V.[Vishwesh], Xu, D.G.[Da-Guang], Dou, Q.[Qi], Xu, Z.[Ziyue],
Fair Federated Medical Image Segmentation via Client Contribution Estimation,
CVPR23(16302-16311)
IEEE DOI 2309
BibRef

Wang, Y.C.[Yong-Chao], Xiao, B.[Bin], Bi, X.L.[Xiu-Li], Li, W.S.[Wei-Sheng], Gao, X.B.[Xin-Bo],
MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation,
CVPR23(15651-15660)
IEEE DOI 2309
BibRef

Bai, Y.H.[Yun-Hao], Chen, D.[Duowen], Li, Q.L.[Qing-Li], Shen, W.[Wei], Wang, Y.[Yan],
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation,
CVPR23(11514-11524)
IEEE DOI 2309
BibRef

Rahman, A.[Aimon], Valanarasu, J.M.J.[Jeya Maria Jose], Hacihaliloglu, I.[Ilker], Patel, V.M.[Vishal M.],
Ambiguous Medical Image Segmentation Using Diffusion Models,
CVPR23(11536-11546)
IEEE DOI 2309
BibRef

Basak, H.[Hritam], Yin, Z.Z.[Zhao-Zheng],
Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation,
CVPR23(19786-19797)
IEEE DOI 2309
BibRef

Santhirasekaram, A.[Ainkaran], Winkler, M.[Mathias], Rockall, A.[Andrea], Glocker, B.[Ben],
Topology Preserving Compositionality for Robust Medical Image Segmentation,
TAG-PRA23(543-552)
IEEE DOI 2309
BibRef

Yuan, M.Z.[Ming-Ze], Xia, Y.[Yingda], Dong, H.X.[He-Xin], Chen, Z.[Zifan], Yao, J.[Jiawen], Qiu, M.Y.[Ming-Yan], Yan, K.[Ke], Yin, X.L.[Xiao-Li], Shi, Y.[Yu], Chen, X.[Xin], Liu, Z.[Zaiyi], Dong, B.[Bin], Zhou, J.[Jingren], Lu, L.[Le], Zhang, L.[Ling], Zhang, L.[Li],
Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization,
CVPR23(23879-23889)
IEEE DOI 2309
BibRef

Jeong, S.W.[Seung-Wan], Cho, H.H.[Hwan-Ho], Kwon, J.[Junmo], Park, H.[Hyunjin],
Region-of-interest Attentive Heteromodal Variational Encoder-decoder for Segmentation with Missing Modalities,
ACCV22(VI:132-148).
Springer DOI 2307
BibRef

Zheng, Z.[Zhou], Hayashi, Y.[Yuichiro], Oda, M.[Masahiro], Kitasaka, T.[Takayuki], Mori, K.[Kensaku],
Trimix: A General Framework for Medical Image Segmentation from Limited Supervision,
ACCV22(VI:185-202).
Springer DOI 2307
BibRef

Tian, M.[Mu], Yang, Q.[Qinzhu], Gao, Y.[Yi],
Multi-scale Multi-task Distillation for Incremental 3d Medical Image Segmentation,
MCV22(369-384).
Springer DOI 2304
BibRef

Salpea, N.[Natalia], Tzouveli, P.[Paraskevi], Kollias, D.[Dimitrios],
Medical Image Segmentation: A Review of Modern Architectures,
MIA-COVID19D22(691-708).
Springer DOI 2304
BibRef

Wang, Z.Y.[Zi-Yang], Li, T.Z.[Tian-Ze], Zheng, J.Q.[Jian-Qing], Huang, B.[Baoru],
When CNN Meet with VIT: Towards Semi-supervised Learning for Multi-class Medical Image Semantic Segmentation,
MIA-COVID19D22(424-441).
Springer DOI 2304
BibRef

Tragakis, A.[Athanasios], Kaul, C.[Chaitanya], Murray-Smith, R.[Roderick], Husmeier, D.[Dirk],
The Fully Convolutional Transformer for Medical Image Segmentation,
WACV23(3649-3658)
IEEE DOI 2302
Convolutional codes, Image segmentation, Technological innovation, Semantic segmentation, Semantics, Applications: Biomedical/healthcare/medicine BibRef

Heidari, M.[Moein], Kazerouni, A.[Amirhossein], Soltany, M.[Milad], Azad, R.[Reza], Aghdam, E.K.[Ehsan Khodapanah], Cohen-Adad, J.[Julien], Merhof, D.[Dorit],
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation,
WACV23(6191-6201)
IEEE DOI 2302
Image segmentation, Correlation, Convolution, Computational modeling, Transformers, Biomedical/healthcare/medicine BibRef

Rahman, M.M.[Md Mostafijur], Marculescu, R.[Radu],
Medical Image Segmentation via Cascaded Attention Decoding,
WACV23(6211-6220)
IEEE DOI 2302
Image segmentation, Medical services, Logic gates, Transformers, Decoding, Lesions, Applications: Biomedical/healthcare/medicine BibRef

Cho, W.W.[Won-Woo], Park, J.[Jeonghoon], Choo, J.[Jaegul],
Training Auxiliary Prototypical Classifiers for Explainable Anomaly Detection in Medical Image Segmentation,
WACV23(2623-2632)
IEEE DOI 2302
Training, Image segmentation, Machine learning algorithms, Pipelines, Training data, Network architecture, Data processing, visual reasoning BibRef

Guo, P.F.[Peng-Fei], Yang, D.[Dong], Hatamizadeh, A.[Ali], Xu, A.[An], Xu, Z.[Ziyue], Li, W.Q.[Wen-Qi], Zhao, C.[Can], Xu, D.[Daguang], Harmon, S.[Stephanie], Turkbey, E.[Evrim], Turkbey, B.[Baris], Wood, B.[Bradford], Patella, F.[Francesca], Stellato, E.[Elvira], Carrafiello, G.[Gianpaolo], Patel, V.M.[Vishal M.], Roth, H.R.[Holger R.],
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation,
ECCV22(XXI:437-455).
Springer DOI 2211
BibRef

Wang, W.X.[Wen-Xuan], Chen, C.[Chen], Wang, J.[Jing], Zha, S.[Sen], Zhang, Y.[Yan], Li, J.Y.[Jiang-Yun],
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation,
ECCV22(XXI:506-522).
Springer DOI 2211
BibRef

Zhou, Z.Q.[Zi-Qi], Qi, L.[Lei], Shi, Y.[Yinghuan],
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration,
ECCV22(XXI:420-436).
Springer DOI 2211
BibRef

Gupta, S.[Saumya], Hu, X.L.[Xiao-Ling], Kaan, J.[James], Jin, M.[Michael], Mpoy, M.[Mutshipay], Chung, K.[Katherine], Singh, G.[Gagandeep], Saltz, M.[Mary], Kurc, T.[Tahsin], Saltz, J.[Joel], Tassiopoulos, A.[Apostolos], Prasanna, P.[Prateek], Chen, C.[Chao],
Learning Topological Interactions for Multi-Class Medical Image Segmentation,
ECCV22(XXIX:701-718).
Springer DOI 2211
BibRef

Liu, L.[Libo], Fan, X.X.[Xin-Xin], Zhang, X.D.[Xiao-Dong], Hu, Q.M.[Qing-Mao],
Lightweight Dual-Domain Network for Real-Time Medical Image Segmentation,
ICIP22(396-400)
IEEE DOI 2211
Image segmentation, Convolution, Frequency-domain analysis, Semantics, Surgery, Stroke (medical condition), Real-time systems, lightweight dual-domain network BibRef

Cheng, J.L.[Jun-Long], Gao, C.[Chengrui], Li, C.[Changlin], Ming, Z.Q.[Zhang-Qiang], Yang, Y.[Yong], Wang, F.J.[Feng-Jie], Zhu, M.[Min],
F2RNET: A Full-Resolution Representation Network for Biomedical Image Segmentation,
ICIP22(2406-2410)
IEEE DOI 2211
Deep learning, Image segmentation, Image resolution, Convolution, Multilayer perceptrons, Feature extraction, Transformers, Biomedical image segmentation BibRef

Wu, H.[Huisi], Xiao, F.Y.[Fang-Yan], Liang, C.X.[Chong-Xin],
Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation,
ECCV22(XX:417-434).
Springer DOI 2211
BibRef

Zhao, Z.Y.[Zi-Yuan], Zhu, A.D.[An-Dong], Zeng, Z.[Zeng], Veeravalli, B.[Bharadwaj], Guan, C.T.[Cun-Tai],
ACT-NET: Asymmetric Co-Teacher Network for Semi-Supervised Memory-Efficient Medical Image Segmentation,
ICIP22(1426-1430)
IEEE DOI 2211
Knowledge engineering, Image segmentation, Limiting, Computational modeling, Employment, Data models, medical image segmentation BibRef

Khan, T.M.[Tariq M.], Robles-Kelly, A.[Antonio], Naqvi, S.S.[Syed S.],
T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation,
WACV22(1799-1808)
IEEE DOI 2202
Image segmentation, Skin, Retinal vessels, Mobile handsets, Lesions, Convolutional neural networks, Image Processing BibRef

Hatamizadeh, A.[Ali], Tang, Y.C.[Yu-Cheng], Nath, V.[Vishwesh], Yang, D.[Dong], Myronenko, A.[Andriy], Landman, B.[Bennett], Roth, H.R.[Holger R.], Xu, D.G.[Da-Guang],
UNETR: Transformers for 3D Medical Image Segmentation,
WACV22(1748-1758)
IEEE DOI 2202
Image segmentation, Semantics, Computer architecture, Transformers, Natural language processing, Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy BibRef

Shi, D.C.[Da-Chuan], Liu, R.Y.[Rui-Yang], Tao, L.M.[Lin-Mi], He, Z.X.[Zuo-Xiang], Huo, L.[Li],
Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation,
ICIP21(31-35)
IEEE DOI 2201
Training, Manifolds, Image segmentation, Neural networks, Feature extraction, Decoding, Data mining, Convolutional neural networks BibRef

Koker, T.[Teddy], Mireshghallah, F.[Fatemehsadat], Titcombe, T.[Tom], Kaissis, G.[Georgios],
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation,
ICIP21(394-398)
IEEE DOI 2201
Deep learning, Image segmentation, Sensitivity, Computed tomography, Decision making, Distance measurement, Medical Imaging BibRef

Bhide, S., Mikut, R., Leptin, M., Stegmaier, J.,
Semi-Automatic Generation Of Tight Binary Masks And Non-Convex Isosurfaces For Quantitative Analysis of 3D Biological Samples,
ICIP20(2820-2824)
IEEE DOI 2011
Image segmentation, Embryo, Shape, Isosurfaces, GUI BibRef

Chang, Q., Qu, H., Zhang, Y., Sabuncu, M., Chen, C., Zhang, T., Metaxas, D.N.,
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data,
CVPR20(13853-13863)
IEEE DOI 2008
Biomedical imaging, Generators, Data privacy, Task analysis, Image segmentation, Data models BibRef

Smith, T.J.[Thomas J.], Valstar, M.[Michel], Sharkey, D.[Don], Crowe, J.[John],
Clinical Scene Segmentation with Tiny Datasets,
CVPM19(1637-1645)
IEEE DOI 2004
convolutional neural nets, graph theory, image representation, image segmentation, learning (artificial intelligence), End to end BibRef

Xu, X., Lu, Q., Yang, L., Hu, S., Chen, D., Hu, Y., Shi, Y.,
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation,
CVPR18(8300-8308)
IEEE DOI 1812
Quantization (signal), Training, Biomedical imaging, Image segmentation, Uncertainty, Memory management, Neural networks BibRef

Kromp, F., Ambros, I., Weiss, T., Bogen, D., Dodig, H., Berneder, M., Gerber, T., Taschner-Mandl, S., Ambros, P., Hanbury, A.,
Machine learning framework incorporating expert knowledge in tissue image annotation,
ICPR16(343-348)
IEEE DOI 1705
Algorithm design and analysis, Biological tissues, Image segmentation, Machine learning algorithms, Morphology, Prediction algorithms, image annotation, machine learning, online, training BibRef

Mesbah, S., Shalaby, A., Willhite, A., Harkema, S., Rejc, E., El-baz, A.,
Automatic 3-D muscle and fat segmentation of thigh magnetic resonance images in individuals with spinal cord injury,
ICIP17(3280-3284)
IEEE DOI 1803
Markov processes, biomedical MRI, diseases, image registration, image segmentation, injuries, medical disorders, SCI BibRef

Jarrar, M., Kerkeni, A., Abdallah, A.B., Bedoui, M.H.,
MLP Neural Network Classifier for Medical Image Segmentation,
CGiV16(88-93)
IEEE DOI 1608
image classification BibRef

Zhu, H.[Hong], Xu, J.H.[Jin-Hui], Hu, J.F.[Jun-Feng], Chen, J.[Jing],
Medical Image Segmentation Using Improved Affinity Propagation,
CompIMAGE16(208-215).
Springer DOI 1704
Affinity Propagation (AP) vs. Nearest Neighbor classification. BibRef

Masci, J.[Jonathan], Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Fricout, G.[Gabriel], Schmidhuber, J.[Jurgen],
A fast learning algorithm for image segmentation with max-pooling convolutional networks,
ICIP13(2713-2717)
IEEE DOI 1402
Convolutional Network BibRef

Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Masci, J.[Jonathan], Gambardella, L.M.[Luca M.], Schmidhuber, J.[Jurgen],
Fast image scanning with deep max-pooling convolutional neural networks,
ICIP13(4034-4038)
IEEE DOI 1402
Biomedical Imaging BibRef

Ding, J.J.[Jian-Jiun], Wang, Y.H.[Yu-Hsiang], Hu, L.L.[Lee-Lin], Chao, W.L.[Wei-Lun], Shau, Y.W.[Yio-Wha],
Muscle injury determination by image segmentation,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Kamarainen, J.K.[Joni-Kristian], Lensu, L.[Lasse], Kauppi, T.[Tomi],
Combining Multiple Image Segmentations by Maximizing Expert Agreement,
MLMI12(193-200).
Springer DOI 1211
BibRef

Pham, T.D.[Tuan D.], Eisenblatter, U.[Uwe], Golledge, J.[Jonathan], Baune, B.T.[Bernhard T.], Berger, K.[Klaus],
Segmentation of medical images using geo-theoretic distance matrix in fuzzy clustering,
ICIP09(3369-3372).
IEEE DOI 0911
BibRef

Luong, H.V.[Hyunh Van], Kim, J.M.[Jong Myon],
A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation,
ISVC08(I: 1092-1101).
Springer DOI 0812
BibRef

Vannier, M.W.[Michael W.], Haller, J.W.,
Biomedical image segmentation,
ICIP98(II: 20-24).
IEEE DOI 9810
BibRef

Wegner, S., Harms, T., Oswald, H., Fleck, E.,
The watershed transformation on graphs for the segmentation of CT images,
ICPR96(III: 498-502).
IEEE DOI 0509
BibRef
Earlier:
Medical image segmentation using the watershed transformation on graphs,
ICIP96(III: 37-40).
IEEE DOI 9610
Image Segmentation for a Hyperthermia Planning Environment BibRef

Wegner, S., Harms, T., Builtjes, J.H., Oswald, H., Fleck, E.,
The watershed transformation for multiresolution image segmentation,
CIAP95(31-36).
Springer DOI 9509
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Weakly Supervised, Self Supervised Semantic Segmentation .


Last update:Jan 30, 2024 at 20:33:16