| Autor: |
Freyre CAC; 33413Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland., Spiegel S; 1528Novartis International AG, Basel, Switzerland., Gubser Keller C; 33413Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland., Vandemeulebroecke M; 111826Novartis Pharma AG, Basel, Switzerland., Hoefling H; 33413Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland., Dubost V; 33413Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland., Cörek E; 130383University of Basel, Basel, Switzerland., Moulin P; 33413Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland., Hossain I; 33413Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland. |
| Abstrakt: |
Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks. |
