Certainty Pooling for Multiple Instance Learning

Gildenblat, Jacob; Ido, Ben-Shaul,; Zvi, Lapp,; Klaiman, Eldad

doi:10.1007/978-3-030-68763-2_11

Cited by 3 publications

(2 citation statements)

References 8 publications

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Section: Classificationmentioning

confidence: 99%

“…For breast cancer, a key task is understanding whether the WSI contains mitotic or non-mitotic cells, 89,[92][93][94][95][96][97][98][99][100][101][102] or tumorous cells. 103,102 Studies have explored the use of ML to distinguish breast cancer WSIs between normal, benign, carcinoma in situ (CIS) and breast invasive carcinoma (BIC). 104 Classification has also been used to identify specific subtypes of immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2), 105,106 Ductal or Lobular, Basal-like or non-Basal-like, and different tumor grades.…”

Section: Classificationmentioning

confidence: 99%

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Machine learning in computational histopathology: Challenges and opportunities

Cooper

Krishnan

2023

Genes Chromosomes & Cancer

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Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.

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