Titlebook: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clin; Second International Kenji Suzuki,Mau

morale

Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimh Alzheimer’s disease and healthy controls. Afterwards, we produced attribution maps for each subject in the test data and quantitatively compared them across models and attribution methods. We show that visual comparison is not sufficient and that some widely used attribution methods produce highly inconsistent outcomes.

雜役

UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomicsensional spaces. To bridge the gap with radiomics-based models, we implement a regression concept vector showing the impact of radiomic features on the predictions of deep networks. In addition, we introduce a new metric with improved scaling to high-dimensional spaces, allowing comparison across multiple layers.

香料

Demulcent

Deep Learning Based Multi-modal Registration for Retinal Imagingour approach using manual grading by expert readers. In the largest dataset (FA-to-SLO/OCT) containing 1130 pairs we achieve an average error rate of 13.12%. We compared our method with intensity based affine registration methods using original and vessel segmentation images.

過于平凡

esoteric

Towards Automatic Diagnosis from Multi-modal Medical Datacal dataset, we show that combining features from images (e.g. x-rays) and texts (e.g. clinical reports), sharing information among different tasks (e.g. x-rays classification, autoencoder, and diagnosis generation) and across domains boost the performance of diagnosis generation (86.0% in terms of BLEU@4).