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Image segmentation, Biomedical imaging, Training, Annotations,
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Image segmentation, Training, Annotations, Shape, Biomedical imaging,
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Image segmentation, Computer architecture, Semantics,
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2203
Uncertainty, Image segmentation, Estimation, Training,
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Image segmentation, Task analysis, Training, Biomedical imaging,
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Medical image segmentation, Dynamic scale-aware context,
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Code, Segmentation.
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Medical diagnostic imaging, Decoding, neural network
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2209
Feature extraction, Image reconstruction, Semantics,
Image segmentation, Convolution, Biomedical imaging, Task analysis,
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2212
Image segmentation, Pathology, Training, Manuals, Annotations,
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MsVRL: Self-Supervised Multiscale Visual Representation Learning via
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2301
Image segmentation, Task analysis, Visualization,
Self-supervised learning, Medical diagnostic imaging, abdomen
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IEEE DOI
2301
Image segmentation, Adaptation models, Head, Biomedical imaging,
Convolution, Training, Data models, Domain generalization,
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2301
Deep learning, Segmentation, Medical imaging, Loss function
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2301
Transformer, Medical image segmentation,
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IEEE DOI
2303
Annotations, Image segmentation, Training, Biomedical imaging,
Task analysis, Shape, 3D medical image segmentation,
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CVPR23(3302-3311)
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2309
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Sun, M.[Muyi],
Sun, Z.A.[Zhen-An],
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Graph Flow: Cross-Layer Graph Flow Distillation for Dual Efficient
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IEEE DOI
2304
Image segmentation, Knowledge engineering,
Medical diagnostic imaging, Sun, Cross layer design, Annotations, graph flow
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Wang, Y.B.[Yan-Bo],
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LSUnetMix: Fuse channel feature information with long-short term
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2304
biomedical engineering, image segmentation,
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2305
Medical image segmentation, Geometric structure learning,
Adversarial learning, Computer-Aided diagnosis (CAD)
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Li, H.[He],
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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
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Song, Y.Y.[You-Yi],
Yu, L.[Lequan],
Lei, B.Y.[Bai-Ying],
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Data Discernment for Affordable Training in Medical Image
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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
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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
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MedImg(42), No. 5, May 2023, pp. 1484-1494.
IEEE DOI
2305
Transformers, Image segmentation, Task analysis, Bridges,
Medical diagnostic imaging, Feature extraction, Merging,
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Hao, D.[Dechen],
Li, H.L.[Hua-Ling],
A graph-based edge attention gate medical image segmentation method,
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DOI Link
2305
dropout residual graph convolution block, edge attention gate,
medical image segmentation, UNet++, weighted loss function
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Li, Z.[Zeju],
Kamnitsas, K.[Konstantinos],
Ouyang, C.[Cheng],
Chen, C.[Chen],
Glocker, B.[Ben],
Context Label Learning: Improving Background Class Representations in
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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
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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
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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
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Shu, Y.C.[Yu-Cheng],
Li, H.[Hengbo],
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Bi, X.L.[Xiu-Li],
Li, W.S.[Wei-Sheng],
Cross-Mix Monitoring for Medical Image Segmentation With Limited
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MultMed(25), 2023, pp. 1700-1712.
IEEE DOI
2306
Image segmentation, Biomedical imaging, Training, Data models,
Perturbation methods, Task analysis, Monitoring, transductive monitor
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Chen, H.P.[Hai-Peng],
Lyu, Y.D.[Ying-Da],
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Elsevier DOI
2306
Complementary feature, Contrastive feature, Mutual attention,
Synthetic data augmentation, Medical image segmentation
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Yang, X.[Xin],
Cheng, K.T.[Kwang-Ting],
FedMix: Mixed Supervised Federated Learning for Medical Image
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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
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Jiang, M.[Meirui],
Yang, H.Z.[Hong-Zheng],
Cheng, C.[Chen],
Dou, Q.[Qi],
IOP-FL: Inside-Outside Personalization for Federated Medical Image
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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
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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
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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
Pang, Y.[Yan],
Liang, J.[JiaMing],
Huang, T.[Teng],
Chen, H.[Hao],
Li, Y.H.[Yun-Hao],
Li, D.[Dan],
Huang, L.[Lin],
Wang, Q.[Qiong],
Slim UNETR: Scale Hybrid Transformers to Efficient 3D Medical Image
Segmentation Under Limited Computational Resources,
MedImg(43), No. 3, March 2024, pp. 994-1005.
IEEE DOI Code:
WWW Link.
2403
Biomedical imaging, Transformers, Image segmentation, Task analysis,
Computational modeling, Solid modeling, resource-limited application
BibRef
Han, X.J.[Xian-Jun],
Li, T.T.[Tian-Tian],
Bai, C.[Can],
Yang, H.Y.[Hong-Yu],
Integrating prior knowledge into a bibranch pyramid network for
medical image segmentation,
IVC(143), 2024, pp. 104945.
Elsevier DOI
2403
Image pyramid, Medical image segmentation, Prior knowledge,
Medical image processing
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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],
Corté, L.[Laurent],
Tillier, Y.[Yannick],
Decencière, 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],
Reiß, 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
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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
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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
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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
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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 .