PathoSpotter-Search: A Content-Based Image Retrieval Tool for Nephropathology
Nephropathologists typically organizes their repository of digital images of kidney biopsies in such a way that it is difficult to retrieve cases that have images similar to a picture under analysis. Having this in mind, we initiated the development of PathoSpotter-Search, a Content-Based Image Retrieval system for images of kidney biopsies. The system operates as a cloud service to avoid the need to install any software on the pathologist’s computer. Our approach combines a feature extractor followed by a similarity score calculator. We evaluated convolutional network (CN) architectures (VGG-16 (original and fine-tuned) and Inception-ResNet, and a network used in the proprietary classifier for glomerular hypercellularity), combined with Cosine and Euclidean distances as similarity scores. The first results have shown that the CN of the VGG- 16 combined with cosine distance yielded the best performance (precision 53%). To assess the usability and functionality of the PathoSpotter-Search as a cloud service, the system was tested by nephropathologists and proved to be useful as a tool for retrieving similar images from their local repositories. Currently, we are working to improve the system precision to at least 70%, and evaluating strategies to retrieve similar images based on segments or tiles of the query image.
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