Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review Article Swipe

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Ahmad Obeid , Said Boumaraf , Anabia Sohail , Taimur Hassan , Sajid Javed , Jorge Dias , Mohammed Bennamoun , Naoufel Werghi ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.19820

Recent years witnessed remarkable progress in computational histopathology, largely fueled by deep learning. This brought the clinical adoption of deep learning-based tools within reach, promising significant benefits to healthcare, offering a valuable second opinion on diagnoses, streamlining complex tasks, and mitigating the risks of inconsistency and bias in clinical decisions. However, a well-known challenge is that deep learning models may contain up to billions of parameters; supervising their training effectively would require vast labeled datasets to achieve reliable generalization and noise resilience. In medical imaging, particularly histopathology, amassing such extensive labeled data collections places additional demands on clinicians and incurs higher costs, which hinders the art's progress. Addressing this challenge, researchers devised various strategies for leveraging deep learning with limited data and annotation availability. In this paper, we present a comprehensive review of deep learning applications in histopathology, with a focus on the challenges posed by data scarcity over the past decade. We systematically categorize and compare various approaches, evaluate their distinct contributions using benchmarking tables, and highlight their respective advantages and limitations. Additionally, we address gaps in existing reviews and identify underexplored research opportunities, underscoring the potential for future advancements in this field.

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Scarcity Histopathology Data science Computer science Economics Medicine Pathology Microeconomics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2410.19820
PDF: https://arxiv.org/pdf/2410.19820
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4404313480

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DOI: https://doi.org/10.48550/arxiv.2410.19820

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Title: Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-10-18

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Authors: Ahmad Obeid, Said Boumaraf, Anabia Sohail, Taimur Hassan, Sajid Javed, Jorge Dias, Mohammed Bennamoun, Naoufel Werghi

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Landing page: https://arxiv.org/abs/2410.19820

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Concepts: Scarcity, Histopathology, Data science, Computer science, Economics, Medicine, Pathology, Microeconomics

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Cited by: 1

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Citations by year (recent): 2025: 1

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Related works (count): 10

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