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iMIMIC/MIL3ID/LABELS@MICCAI 2020: Lima, Peru
- Jaime S. Cardoso
, Hien Van Nguyen
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. Lecture Notes in Computer Science 12446, Springer 2020, ISBN 978-3-030-61165-1 - Jaime S. Cardoso
Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing. 1
iMIMIC 2020
- Eren Bora Yilmaz, Alexander Oliver Mader, Tobias Fricke, Jaime Peña, Claus-Christian Glüer, Carsten Meyer:
Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers. 3-12 - Andreas P. Hinterreiter, Marc Streit, Bernhard Kainz
Projective Latent Interventions for Understanding and Fine-Tuning Classifiers. 13-22 - Mara Graziani
Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging. 23-32 - Lior Ness, Ella Barkan, Michal Ozery-Flato:
Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations. 33-42 - Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont, Saïd Ladjal, Isabelle Bloch:
Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations. 43-53 - Aniket Joshi, Gaurav Mishra, Jayanthi Sivaswamy:
Explainable Disease Classification via Weakly-Supervised Segmentation. 54-62 - Maximilian Möller, Matthias Kohl, Stefan Braunewell, Florian Kofler, Benedikt Wiestler
Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns. 63-72 - Jing Zhang, Caroline Petitjean, Florian Yger
Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound Images. 73-82
MIL3ID 2020
- Özgün Çiçek, Yassine Marrakchi, Enoch Boasiako Antwi, Barbara Di Ventura, Thomas Brox:
Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins. 85-93 - Long Chen
Semi-supervised Instance Segmentation with a Learned Shape Prior. 94-102 - Angshuman Paul
COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs. 103-111 - Colin B. Hansen, Vishwesh Nath, Riqiang Gao, Camilo Bermudez, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Jeffrey D. Blume, Thomas A. Lasko, Bennett A. Landman:
Semi-supervised Machine Learning with MixMatch and Equivalence Classes. 112-121 - Chongchong Song, Baochun He, Hongyu Chen, Shuangfu Jia, Xiaoxia Chen, Fucang Jia:
Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CT. 122-129 - Han Liu, Can Cui, Dario J. Englot, Benoit M. Dawant:
Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation. 130-137 - Soundarya Krishnan, Rishab Khincha, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan:
A Case Study of Transfer of Lesion-Knowledge. 138-145 - Xintong Li, Huijuan Yang
Transfer Learning with Joint Optimization for Label-Efficient Medical Image Anomaly Detection. 146-154 - Chenyu You, Junlin Yang, Julius Chapiro, James S. Duncan:
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-domain Liver Segmentation. 155-163 - Raja Muhammad Saad Bashir, Talha Qaiser, Shan-E-Ahmed Raza, Nasir M. Rajpoot
HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification. 164-171 - Eduardo H. P. Pooch, Pedro L. Ballester, Rodrigo C. Barros
Semi-supervised Classification of Chest Radiographs. 172-179
LABELS 2020
- Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina:
Risk of Training Diagnostic Algorithms on Data with Demographic Bias. 183-192 - Sebastian Otálora, Niccolò Marini, Henning Müller
Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks. 193-203 - Christof A. Bertram, Mitko Veta, Christian Marzahl
Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels. 204-213 - Zheyu Zhu, Yuzhe Lu, Ruining Deng
EasierPath: An Open-Source Tool for Human-in-the-Loop Deep Learning of Renal Pathology. 214-222 - Chao-Ting Li, Hung-Wen Tsai, Tseng-Lung Yang, Jung-Chi Lin, Nan-Haw Chow
Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection. 223-232 - Chen-Han Tsai, Nahum Kiryati, Eli Konen, Miri Sklair-Levy, Arnaldo Mayer:
Labeling of Multilingual Breast MRI Reports. 233-241 - Tom van Sonsbeek, Veronika Cheplygina:
Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning. 242-253 - David A. Wood
Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study. 254-265 - Jihun Yoon, Jiwon Lee, SungHyun Park, Woo Jin Hyung
Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset. 266-276 - Patrick Schrempf
Paying Per-Label Attention for Multi-label Extraction from Radiology Reports. 277-289
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