Titlebook: Interpretable and Annotation-Efficient Learning for Medical Image Computing; Third International Jaime Cardoso,Hien Van Nguyen,Samaneh Abb

諷刺

978-3-030-61165-1Springer Nature Switzerland AG 2020

不整齊

Interpretable and Annotation-Efficient Learning for Medical Image Computing978-3-030-61166-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

旁觀者

傳染

sacrum

anaerobic

Presbycusis

Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical ImagingWith feature transfer being a common practice, scale-invariant features implicitly learned from pretraining on ImageNet tend to be preferred over scale-covariant features. The pruning strategy in this paper proposes a way to maintain scale covariance in the transferred features. Deep learning interp

Halfhearted

含糊其辭

Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Ievel, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the contex

DRILL

Explainable Disease Classification via Weakly-Supervised Segmentationhese systems achieve high to very high accuracy in specific disease detection for which they are trained but lack in terms of an explanation for the provided decision/classification result. The activation maps which correspond to decisions do not correlate well with regions of interest for specific