Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review
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arXiv
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| Format: | Preprint |
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2024
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| author | Obeid, Ahmad Boumaraf, Said Sohail, Anabia Hassan, Taimur Javed, Sajid Dias, Jorge Bennamoun, Mohammed Werghi, Naoufel |
| author_facet | Obeid, Ahmad Boumaraf, Said Sohail, Anabia Hassan, Taimur Javed, Sajid Dias, Jorge Bennamoun, Mohammed Werghi, Naoufel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19820 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review Obeid, Ahmad Boumaraf, Said Sohail, Anabia Hassan, Taimur Javed, Sajid Dias, Jorge Bennamoun, Mohammed Werghi, Naoufel Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.19820 |