mlsanityData to 14 Oct 2026

A review of machine learning approaches, challenges and prospects for computational tumor pathology

arXiv 2206.01728 ↗First posted 31 May 2022eess.IV cs.AI cs.CVcited by 3

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Abstract (arXiv)

Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the past decade, rapid advances in artificial intelligence, chip design and manufacturing, and mobile computing have facilitated research in computational pathology and have the potential to provide better-integrated solutions for whole-slide images, multi-omics data, and clinical informatics. However, tumor computational pathology now brings some challenges to the application of tumour screening, diagnosis and prognosis in terms of data integration, hardware processing, network sharing bandwidth and machine learning technology. This review investigates image preprocessing methods in computational pathology from a pathological and technical perspective, machine learning-based methods, and applications of computational pathology in breast, colon, prostate, lung, and various tumour disease scenarios. Finally, the challenges and prospects of machine learning in computational pathology applications are discussed.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 28 of its 202 references that are in the map. First posted on arXiv 31 May 2022.

Builds on

28 of 202 references.

References · 28

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16 more
  1. 2019Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networkscited by 43
  2. 2020A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learningcited by 35
  3. 2020HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classificationcited by 33
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  5. 2018Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standardcited by 25
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  9. 2019Weakly Supervised Prostate TMA Classification via Graph Convolutional Networkscited by 17
  10. 2018RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classificationcited by 11
  11. 2019Double Transfer Learning for Breast Cancer Histopathologic Image Classificationcited by 6
  12. 2020Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Netcited by 5
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  15. 2021Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Netcited by 2
  16. 2022Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detectorcited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2023Rotation-Agnostic Image Representation Learning for Digital Pathologycited by 15
  2. 2025RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterizationcited by 2
  3. 2025A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Imagescited by 1