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ORCIDiMIMIC@MICCAI 2022: Singapore
- Mauricio Reyes
, Pedro Henriques Abreu, Jaime S. Cardoso:
Interpretability of Machine Intelligence in Medical Image Computing - 5th International Workshop, iMIMIC 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings. Lecture Notes in Computer Science 13611, Springer 2022, ISBN 978-3-031-17975-4 - Hanxiao Zhang, Liang Chen, Minghui Zhang, Xiao Gu, Yulei Qin, Weihao Yu, Feng Yao, Zhexin Wang, Yun Gu, Guang-Zhong Yang:
Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging. 1-11 - DaeHyun Cho, Christian Wallraven:
Do Pre-processing and Augmentation Help Explainability? A Multi-seed Analysis for Brain Age Estimation. 12-21 - Florian Kowarsch, Lisa Weijler, Matthias Wödlinger, Michael Reiter, Margarita Maurer-Granofszky, Angela Schumich, Elisa O. Sajaroff, Stefanie Groeneveld-Krentz, Jorge G. Rossi, Leonid Karawajew, Richard Ratei, Michael N. Dworzak:
Towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data. 22-32 - Jiahao Lu, Chong Yin, Oswin Krause, Kenny Erleben, Michael Bachmann Nielsen, Sune Darkner:
Reducing Annotation Need in Self-explanatory Models for Lung Nodule Diagnosis. 33-43 - Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller, María Rodríguez Martínez:
Attention-Based Interpretable Regression of Gene Expression in Histology. 44-60 - Benjamin Lambert, Florence Forbes, Senan Doyle, Alan Tucholka, Michel Dojat:
Beyond Voxel Prediction Uncertainty: Identifying Brain Lesions You Can Trust. 61-70 - Paul Engstler, Matthias Keicher, David Schinz, Kristina Mach, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Ashkan Khakzar, Nassir Navab:
Interpretable Vertebral Fracture Diagnosis. 71-81 - Rosa C. J. Kraaijveld, Marielle E. P. Philippens, Wietse S. C. Eppinga, Ina M. Jürgenliemk-Schulz, Kenneth G. A. Gilhuijs, Petra S. Kroon, Bas H. M. van der Velden:
Multi-modal Volumetric Concept Activation to Explain Detection and Classification of Metastatic Prostate Cancer on PSMA-PET/CT. 82-92 - Dongyang Kuang, Craig Michoski:
KAM - A Kernel Attention Module for Emotion Classification with EEG Data. 93-103 - Amy Rafferty, Rudolf Nenutil, Ajitha Rajan:
Explainable Artificial Intelligence for Breast Tumour Classification: Helpful or Harmful. 104-123
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