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ORCIDiMIMIC/MIL3ID/LABELS@MICCAI 2020: Lima, Peru
- Jaime S. Cardoso
, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo P. M. Cruz, José Pereira Amorim, Vishal Patel, Badri Roysam, S. Kevin Zhou, Steve B. Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi-Sureshjani:
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, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo P. M. Cruz, José Pereira Amorim, Vishal Patel, Badri Roysam, S. Kevin Zhou, Steve B. Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi-Sureshjani:
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, Thomas Lompech, Henning Müller, Adrien Depeursinge, Vincent Andrearczyk:
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, Jan S. Kirschke, Björn H. Menze, Marie Piraud:
Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns. 63-72 - Jing Zhang, Caroline Petitjean, Florian Yger, Samia Ainouz:
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, Weiwen Zhang, Yuli Wu, Martin Strauch, Dorit Merhof:
Semi-supervised Instance Segmentation with a Learned Shape Prior. 94-102 - Angshuman Paul, Thomas C. Shen, Niranjan Balachandar, Yuxing Tang, Yifan Peng, Zhiyong Lu, Ronald M. Summers:
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, Zhiping Lin, Pavitra Krishnaswamy:
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, Manfredo Atzori:
Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks. 193-203 - Christof A. Bertram, Mitko Veta, Christian Marzahl, Nikolas Stathonikos, Andreas K. Maier, Robert Klopfleisch, Marc Aubreville:
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, Haichun Yang, Agnes B. Fogo, Yuankai Huo:
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, Yu Hen Hu, Kuo-Sheng Cheng, Pau-Choo Chung:
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, Sina Kafiabadi, Aisha Al Busaidi, Emily Guilhem, Jeremy Lynch, Matthew Townend, Antanas Montvila, Juveria Siddiqui, Naveen Gadapa, Matthew Benger, Gareth J. Barker, Sébastien Ourselin, James H. Cole, Thomas C. Booth:
Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study. 254-265 - Jihun Yoon, Jiwon Lee, SungHyun Park, Woo Jin Hyung, Min-Kook Choi:
Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset. 266-276 - Patrick Schrempf, Hannah Watson, Shadia Mikhael, Maciej Pajak, Matús Falis, Aneta Lisowska, Keith W. Muir, David Harris-Birtill, Alison Q. O'Neil:
Paying Per-Label Attention for Multi-label Extraction from Radiology Reports. 277-289
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