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ORCID2nd MILLanD@MICCAI 2023: Vancouver, BC, Canada
- Zhiyun Xue
, Sameer K. Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang:
Medical Image Learning with Limited and Noisy Data - Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. Lecture Notes in Computer Science 14307, Springer 2023, ISBN 978-3-031-47196-4
Efficient Annotation and Training Strategies
- Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg:
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. 3-13 - Yijie Qu, Qianfei Zhao, Linda Wei, Tao Lu, Shaoting Zhang, Guotai Wang:
ScribSD: Scribble-Supervised Fetal MRI Segmentation Based on Simultaneous Feature and Prediction Self-distillation. 14-23 - Nazanin Moradinasab, Rebecca A. Deaton, Laura S. Shankman, Gary K. Owens, Donald E. Brown:
Label-Efficient Contrastive Learning-Based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images. 24-34 - Christopher Adnel, Islem Rekik:
Affordable Graph Neural Network Framework Using Topological Graph Contraction. 35-46
Approaches for Noisy, Missing, and Low Quality Data
- Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Qiong Liu, Albert J. Sinusas, Chi Liu:
Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT. 49-59 - Yang Yu, Jiahao Wang, Ashish Jith Sreejith Kumar, Bryan Tan, Navya Vanjavaka, Nurul Hafidzah Rahim, Alistair Koh, Shaheen Low, Yih Yian Sitoh, Hanry Yu, Pavitra Krishnaswamy, Ivan Ho Mien:
A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging. 60-70 - Maryna Kvasnytsia, Abel Díaz Berenguer, Hichem Sahli, Jef Vandemeulebroucke:
COVID-19 Lesion Segmentation Framework for the Contrast-Enhanced CT in the Absence of Contrast-Enhanced CT Annotations. 71-81 - Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Joseph T. Roland, Ken S. Lau, Qi Liu, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo:
Feasibility of Universal Anomaly Detection Without Knowing the Abnormality in Medical Images. 82-92
Unsupervised, Self-supervised, and Contrastive Learning
- Camille Ruppli, Pietro Gori, Roberto Ardon, Isabelle Bloch:
Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection. 95-105 - Yunsung Chung, Chanho Lim, Chao Huang, Nassir Marrouche, Jihun Hamm:
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation. 106-116 - Tony Xu, Matthew Rozak, Emmanuel E. Ntiri, Adrienne Dorr, James R. Mester, Bojana Stefanovic, Anne L. Martel, Maged Goubran:
Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy. 117-127 - Zhaohui Liang, Zhiyun Xue, Sivaramakrishnan Rajaraman, Feng Yang, Sameer K. Antani:
Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning. 128-137 - Rahul G. S., Sriprabha Ramanarayanan, Mohammad Al Fahim, Keerthi Ram, Preejith S. P, Mohanasankar Sivaprakasam:
SDLFormer: A Sparse and Dense Locality-Enhanced Transformer for Accelerated MR Image Reconstruction. 138-147 - Slim Hachicha, Célia Le, Valentine Wargnier-Dauchelle, Michaël Sdika:
Robust Unsupervised Image to Template Registration Without Image Similarity Loss. 148-157 - Jianfei Liu, Omid Shafaat, Ronald M. Summers:
A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue. 158-167
Weakly-Supervised, Semi-supervised, and Multitask Learning
- Sajith Rajapaksa, Khashayar Namdar, Farzad Khalvati:
Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification. 171-180 - Ziyang Wang, Irina Voiculescu:
Exigent Examiner and Mean Teacher: An Advanced 3D CNN-Based Semi-Supervised Brain Tumor Segmentation Framework. 181-190 - Peidi Xu, Blaire Lee, Olga V. Sosnovtseva, Charlotte Mehlin Sørensen, Kenny Erleben, Sune Darkner:
Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation. 191-201 - Krishna Thoriya, Preeti Mutreja, Sumit Kalra, Angshuman Paul:
Multi-task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Images. 202-210
Active Learning
- Bella Specktor-Fadida, Anna Levchakov, Dana Schonberger, Liat Ben-Sira, Dafna Ben-Bashat, Leo Joskowicz:
Test-Time Augmentation-Based Active Learning and Self-training for Label-Efficient Segmentation. 213-223 - Ji Wu, Zhongfeng Kang, Sebastian Nørgaard Llambias, Mostafa Mehdipour-Ghazi, Mads Nielsen:
Active Transfer Learning for 3D Hippocampus Segmentation. 224-234
Transfer Learning
- Gino Gulamhussene, Oleksii Bashkanov, Jazan Omari, Maciej Pech, Christian Hansen, Marko Rak:
Using Training Samples as Transitive Information Bridges in Predicted 4D MRI. 237-245 - Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian:
To Pretrain or Not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology. 246-256 - Masataka Kawai, Noriaki Ota, Shinsuke Yamaoka:
Large-Scale Pretraining on Pathological Images for Fine-Tuning of Small Pathological Benchmarks. 257-267
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