Abnormality Detection in Histopathology via Density Estimation with Normalising FlowsDownload PDF

Published: 11 May 2021, Last Modified: 16 May 2023MIDL 2021 PosterReaders: Everyone
Keywords: histopathology, out-of-distribution detection, normalising flows
TL;DR: We find lesions in histopathology images by only training on healthy regions.
Abstract: Diagnosis of cancer often relies on the time-consuming examination of histopathology slides by expert pathologists. Automation via supervised deep learning methods require large amounts of pixel-wise annotated data that is costly to acquire. Unsupervised density estimation methods that rely only on the availability of healthy examples could cut down the cost of annotation. We propose to use residual flows as density estimator and compare different tests for out-of-distribution (OOD) detection. Our results suggest that unsupervised OOD detection is a viable approach for detecting suspicious regions in histopathology slides.
Paper Type: both
Primary Subject Area: Application: Histopathology
Secondary Subject Area: Unsupervised Learning and Representation Learning
Paper Status: original work, not submitted yet
Source Code Url: We adapted https://github.com/rtqichen/residual-flows to add a data loader for PatchCamelyon.
Data Set Url: https://github.com/basveeling/pcam
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