Histopathology Slide Indexing and Search: Are We There Yet?
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 29 Jun 2023.
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References · 12
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
- 2017Neural Discrete Representation Learningcited by 4,650
- 2021Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slidescited by 52
- 2019Similar Image Search for Histopathology: SMILYcited by 41
- 2019Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligencecited by 26
- 2016MinMax Radon Barcodes for Medical Image Retrievalcited by 9
- 2015Barcode Annotations for Medical Image Retrieval: A Preliminary Investigationcited by 5
- 2023Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'cited by 5
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