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Image segmentation, Biomedical imaging, Deep learning, Training,
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2110
Image segmentation, Task analysis, Biomedical imaging, Training,
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2110
Image segmentation, Biomedical imaging, Training, Annotations,
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2110
Image segmentation, Training, Annotations, Shape, Biomedical imaging,
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Image Segmentation, Biomedical Image, Full-resolution,
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2206
Image segmentation, Computer architecture, Semantics,
Deep learning, Computational modeling,
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2203
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2203
Uncertainty, Image segmentation, Estimation, Training,
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2211
Image segmentation, Task analysis, Training, Biomedical imaging,
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2K-Fold-Net, EF-Net, U-Net, AFE, Image segmentation
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2205
Medical image segmentation, Dynamic scale-aware context,
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2211
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,
Deep learning, Unsupervised learning, Semantic segmentation,
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Zheng, R.F.[Rui-Feng],
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Yan, S.[Senxiang],
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MsVRL: Self-Supervised Multiscale Visual Representation Learning via
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MedImg(42), No. 1, January 2023, pp. 91-102.
IEEE DOI
2301
Image segmentation, Task analysis, Visualization,
Self-supervised learning, Medical diagnostic imaging, abdomen
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Zhang, J.P.[Jian-Peng],
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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
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Valverde, J.M.[Juan Miguel],
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PR(136), 2023, pp. 109208.
Elsevier DOI
2301
Deep learning, Segmentation, Medical imaging, Loss function
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Yuan, F.N.[Fei-Niu],
Zhang, Z.X.[Zheng-Xiao],
Fang, Z.J.[Zhi-Jun],
An effective CNN and Transformer complementary network for medical
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PR(136), 2023, pp. 109228.
Elsevier DOI
2301
Transformer, Medical image segmentation,
Feature complementary module, Cross-domain fusion, Convolutional Neural Network
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Li, S.[Shumeng],
Cai, H.[Heng],
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Shi, Y.[Yinghuan],
Gao, Y.[Yang],
PLN: Parasitic-Like Network for Barely Supervised Medical Image
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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
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Zou, W.X.[Wen-Xuan],
Qi, X.Q.[Xing-Qun],
Zhou, W.T.[Wan-Ting],
Sun, M.[Muyi],
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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
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Wang, Y.B.[Yan-Bo],
Xiao, Y.[Yunxuan],
LSUnetMix: Fuse channel feature information with long-short term
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IET-CV(17), No. 2, 2023, pp. 241-249.
DOI Link
2304
biomedical engineering, image segmentation,
learning (artificial intelligence)
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Wang, K.[Kun],
Zhang, X.H.[Xiao-Hong],
Lu, Y.T.[Yu-Ting],
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Huang, S.[Sheng],
Yang, D.[Dan],
GSAL: Geometric structure adversarial learning for robust medical
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PR(140), 2023, pp. 109596.
Elsevier DOI
2305
Medical image segmentation, Geometric structure learning,
Adversarial learning, Computer-Aided diagnosis (CAD)
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Li, H.[He],
Iwamoto, Y.[Yutaro],
Han, X.H.[Xian-Hua],
Lin, L.F.[Lan-Fen],
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Chen, Y.W.[Yen-Wei],
3D Multiple-Contextual ROI-Attention Network for Efficient and Accurate
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Fang, W.H.[Wen-Hao],
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Springer DOI
2103
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Lei, T.[Tao],
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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],
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
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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
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
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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
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Salpea, N.[Natalia],
Tzouveli, P.[Paraskevi],
Kollias, D.[Dimitrios],
Medical Image Segmentation: A Review of Modern Architectures,
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Springer DOI
2304
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Wang, Z.[Ziyang],
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
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Cao, H.[Hu],
Wang, Y.[Yueyue],
Chen, J.[Joy],
Jiang, D.S.[Dong-Sheng],
Zhang, X.P.[Xiao-Peng],
Tian, Q.[Qi],
Wang, M.[Manning],
Swin-unet: Unet-like Pure Transformer for Medical Image Segmentation,
MCV22(205-218).
Springer DOI
2304
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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
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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
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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
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Cho, W.W.[Won-Woo],
Park, J.[Jeonghoon],
Choo, J.[Jaegul],
Training Auxiliary Prototypical Classifiers for Explainable Anomaly
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WACV23(2623-2632)
IEEE DOI
2302
Training, Image segmentation, Machine learning algorithms,
Pipelines, Training data, Network architecture, Data processing,
visual reasoning
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Guo, P.F.[Peng-Fei],
Yang, D.[Dong],
Hatamizadeh, A.[Ali],
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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
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Springer DOI
2211
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Wang, J.C.[Jia-Cheng],
Jin, Y.M.[Yue-Ming],
Wang, L.S.[Lian-Sheng],
Personalizing Federated Medical Image Segmentation via Local
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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
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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
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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 .