Histopathology Slide Indexing and Search: Are We There Yet?

Fuente: arXiv
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Auteurs principaux: Shang, Helen H., Nasr, Mohammad Sadegh, Veerla, Jai Prakash, Malidarreh, Parisa Boodaghi, Saurav, MD Jillur Rahman, Hajighasemi, Amir, Huber, Manfred, Moleta, Chace, Makker, Jitin, Luber, Jacob M.
Format: Preprint
Publié: 2023
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author Shang, Helen H.
Nasr, Mohammad Sadegh
Veerla, Jai Prakash
Malidarreh, Parisa Boodaghi
Saurav, MD Jillur Rahman
Hajighasemi, Amir
Huber, Manfred
Moleta, Chace
Makker, Jitin
Luber, Jacob M.
author_facet Shang, Helen H.
Nasr, Mohammad Sadegh
Veerla, Jai Prakash
Malidarreh, Parisa Boodaghi
Saurav, MD Jillur Rahman
Hajighasemi, Amir
Huber, Manfred
Moleta, Chace
Makker, Jitin
Luber, Jacob M.
contents The search and retrieval of digital histopathology slides is an important task that has yet to be solved. In this case study, we investigate the clinical readiness of three state-of-the-art histopathology slide search engines, Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a qualitative assessment of each model's performance in providing retrieval results that are reliable and useful to pathologists. We found that all three image search engines fail to produce consistently reliable results and have difficulties in capturing granular and subtle features of malignancy, limiting their diagnostic accuracy. Based on our findings, we also propose a minimal set of requirements to further advance the development of accurate and reliable histopathology image search engines for successful clinical adoption.
format Preprint
id arxiv_https___arxiv_org_abs_2306_17019
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Histopathology Slide Indexing and Search: Are We There Yet?
Shang, Helen H.
Nasr, Mohammad Sadegh
Veerla, Jai Prakash
Malidarreh, Parisa Boodaghi
Saurav, MD Jillur Rahman
Hajighasemi, Amir
Huber, Manfred
Moleta, Chace
Makker, Jitin
Luber, Jacob M.
Image and Video Processing
Computer Vision and Pattern Recognition
Tissues and Organs
The search and retrieval of digital histopathology slides is an important task that has yet to be solved. In this case study, we investigate the clinical readiness of three state-of-the-art histopathology slide search engines, Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a qualitative assessment of each model's performance in providing retrieval results that are reliable and useful to pathologists. We found that all three image search engines fail to produce consistently reliable results and have difficulties in capturing granular and subtle features of malignancy, limiting their diagnostic accuracy. Based on our findings, we also propose a minimal set of requirements to further advance the development of accurate and reliable histopathology image search engines for successful clinical adoption.
title Histopathology Slide Indexing and Search: Are We There Yet?
topic Image and Video Processing
Computer Vision and Pattern Recognition
Tissues and Organs
url https://arxiv.org/abs/2306.17019