Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

Front Matter ....Pages i-xvii
Front Matter ....Pages 1-1
 Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers  (Eren Bora Yilmaz, Alexander Oliver Mader, Tobias Fricke, Jaime Peña, Claus-Christian Glüer, Carsten Meyer)....Pages 3-12
 Projective Latent Interventions for Understanding and Fine-Tuning Classifiers  (Andreas Hinterreiter, Marc Streit, Bernhard Kainz)....Pages 13-22
 Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging  (Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge, Vincent Andrearczyk)....Pages 23-32
 Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations  (Lior Ness, Ella Barkan, Michal Ozery-Flato)....Pages 33-42
 Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations  (Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont, Saïd Ladjal, Isabelle Bloch)....Pages 43-53
 Explainable Disease Classification via Weakly-Supervised Segmentation  (Aniket Joshi, Gaurav Mishra, Jayanthi Sivaswamy)....Pages 54-62
 Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns  (Maximilian Möller, Matthias Kohl, Stefan Braunewell, Florian Kofler, Benedikt Wiestler, Jan S. Kirschke et al.)....Pages 63-72
 Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound Images  (Jing Zhang, Caroline Petitjean, Florian Yger, Samia Ainouz)....Pages 73-82
Front Matter ....Pages 83-83
 Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins  (Özgün Çiçek, Yassine Marrakchi, Enoch Boasiako Antwi, Barbara Di Ventura, Thomas Brox)....Pages 85-93
 Semi-supervised Instance Segmentation with a Learned Shape Prior  (Long Chen, Weiwen Zhang, Yuli Wu, Martin Strauch, Dorit Merhof)....Pages 94-102
 COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs  (Angshuman Paul, Thomas C. Shen, Niranjan Balachandar, Yuxing Tang, Yifan Peng, Zhiyong Lu et al.)....Pages 103-111
 Semi-supervised Machine Learning with MixMatch and Equivalence Classes  (Colin B. Hansen, Vishwesh Nath, Riqiang Gao, Camilo Bermudez, Yuankai Huo, Kim L. Sandler et al.)....Pages 112-121
 Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CT  (Chongchong Song, Baochun He, Hongyu Chen, Shuangfu Jia, Xiaoxia Chen, Fucang Jia)....Pages 122-129
 Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation  (Han Liu, Can Cui, Dario J. Englot, Benoit M. Dawant)....Pages 130-137
 A Case Study of Transfer of Lesion-Knowledge  (Soundarya Krishnan, Rishab Khincha, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan)....Pages 138-145
 Transfer Learning with Joint Optimization for Label-Efficient Medical Image Anomaly Detection  (Xintong Li, Huijuan Yang, Zhiping Lin, Pavitra Krishnaswamy)....Pages 146-154
 Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-domain Liver Segmentation  (Chenyu You, Junlin Yang, Julius Chapiro, James S. Duncan)....Pages 155-163
 HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification  (Raja Muhammad Saad Bashir, Talha Qaiser, Shan E Ahmed Raza, Nasir M Rajpoot)....Pages 164-171
 Semi-supervised Classification of Chest Radiographs  (Eduardo H. P. Pooch, Pedro Ballester, Rodrigo C. Barros)....Pages 172-179
Front Matter ....Pages 181-181
 Risk of Training Diagnostic Algorithms on Data with Demographic Bias  (Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina)....Pages 183-192
 Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks  (Sebastian Otálora, Niccolò Marini, Henning Müller, Manfredo Atzori)....Pages 193-203
 Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels  (Christof A. Bertram, Mitko Veta, Christian Marzahl, Nikolas Stathonikos, Andreas Maier, Robert Klopfleisch et al.)....Pages 204-213
 EasierPath: An Open-Source Tool for Human-in-the-Loop Deep Learning of Renal Pathology  (Zheyu Zhu, Yuzhe Lu, Ruining Deng, Haichun Yang, Agnes B. Fogo, Yuankai Huo)....Pages 214-222
 Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection  (Chao-Ting Li, Hung-Wen Tsai, Tseng-Lung Yang, Jung-Chi Lin, Nan-Haw Chow, Yu Hen Hu et al.)....Pages 223-232
 Labeling of Multilingual Breast MRI Reports  (Chen-Han Tsai, Nahum Kiryati, Eli Konen, Miri Sklair-Levy, Arnaldo Mayer)....Pages 233-241
 Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning  (Tom van Sonsbeek, Veronika Cheplygina)....Pages 242-253
 Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study  (David A. Wood, Sina Kafiabadi, Aisha Al Busaidi, Emily Guilhem, Jeremy Lynch, Matthew Townend et al.)....Pages 254-265
 Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset  (Jihun Yoon, Jiwon Lee, SungHyun Park, Woo Jin Hyung, Min-Kook Choi)....Pages 266-276
 Paying Per-Label Attention for Multi-label Extraction from Radiology Reports  (Patrick Schrempf, Hannah Watson, Shadia Mikhael, Maciej Pajak, Matúš Falis, Aneta Lisowska et al.)....Pages 277-289
 Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing  (Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva et al.)....Pages C1-C1
Back Matter ....Pages 291-292