Article

Deep Learning Features Encode Interpretable Morphologies within Histological Images

2021-08-17

Abstract excerpt

<h4>ABSTRACT</h4> Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class la...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: d276fb3a-2453-5fd8-8ee8-c97a4397caaf
DOI: 10.1101/2021.08.16.456518

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Deep Learning Features Encode Interpretable Morphologies within Histological ImagesDOI 10.1101/2021.08.16.456518Read

Deep Learning Features Encode Interpretable Morphologies within Histological ImagesSemantic proximity Read

Deep Texture Representations as a Universal Encoder for Pan-cancer HistologySemantic proximity Read

Translating Histopathology Foundation Model Embeddings into Cellular and Molecular Features for Clinical StudiesSemantic proximity Read

Pan-cancer integrative histology-genomic analysis via multimodal deep learningSemantic proximity Read

Predicting cancer outcomes from histology and genomics using convolutional networksSemantic proximity Read

Universal encoding of pan-cancer histology by deep texture representations.Semantic proximity Read

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.Semantic proximity Read

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.Semantic proximity Read

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkersSemantic proximity Read

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE SlidesSemantic proximity Read

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancerSemantic proximity Read

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathologySemantic proximity Read