Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature

Journal of Pathology Informatics
Roger SchaerHenning Müller

Abstract

The introduction of digital pathology into clinical practice has led to the development of clinical workflows with digital images, in connection with pathology reports. Still, most of the current work is time-consuming manual analysis of image areas at different scales. Links with data in the biomedical literature are rare, and a need for search based on visual similarity within whole slide images (WSIs) exists. The main objective of the work presented is to integrate content-based visual retrieval with a WSI viewer in a prototype. Another objective is to connect cases analyzed in the viewer with cases or images from the biomedical literature, including the search through visual similarity and text. An innovative retrieval system for digital pathology is integrated with a WSI viewer, allowing to define regions of interest (ROIs) in images as queries for finding visually similar areas in the same or other images and to zoom in/out to find structures at varying magnification levels. The algorithms are based on a multimodal approach, exploiting both text information and content-based image features. The retrieval system allows viewing WSIs and searching for regions that are visually similar to manually defined ROIs in various data...Continue Reading

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Shambala Image Search Engine ( ParaDISE
trec
CATMAID Annotation Toolkit Informatics Platform
Angular
Pillow
jQuery
OMERO
HyperText Markup Language 5
React
WSI

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