[Paper Review] A review of machine learning approaches, challenges and prospects for computational tumor pathology

Liangrui Pan, Zhichao Feng+1 more|arXiv (Cornell University)|May 31, 2022

AI in cancer detection4 citations

TL;DR

This paper reviews machine learning approaches in computational tumor pathology, focusing on whole-slide image analysis, multi-omics integration, and clinical informatics. It evaluates technical and clinical challenges in data processing, hardware, and model deployment, while outlining future prospects for AI-driven precision oncology.

ABSTRACT

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.

Motivation & Objective

• To synthesize current machine learning techniques applied in computational tumor pathology for cancer diagnosis and prognosis.
• To identify key technical and clinical challenges in integrating whole-slide images, multi-omics data, and clinical informatics.
• To evaluate the role of AI in improving treatment planning, survival prediction, and diagnostic accuracy in oncology.
• To discuss limitations in data sharing, model generalizability, and hardware constraints across clinical settings.
• To outline future research directions for scalable, interpretable, and clinically deployable AI systems in pathology.

Proposed method

• Systematic review of machine learning methods in computational pathology, focusing on image preprocessing and deep learning architectures.
• Analysis of whole-slide image (WSI) processing pipelines, including patch-based and tile-based convolutional neural networks.
• Examination of multi-modal data integration techniques combining genomics, radiomics, pathomics, and clinical data.
• Evaluation of model interpretability and explainability methods such as Grad-CAM and attention mechanisms in histopathology.
• Discussion of federated learning and privacy-preserving AI for secure data sharing across institutions.
• Assessment of hardware and software constraints in deploying models on clinical-grade pathology workstations.

Experimental results

Research questions

• RQ1 What are the dominant machine learning architectures used in whole-slide image analysis for tumor detection and classification?
• RQ2 How do current approaches integrate multi-omics data (e.g., genomics, proteomics) with histopathological images for improved prognosis prediction?
• RQ3 What are the main technical and clinical barriers to deploying machine learning models in routine pathology workflows?
• RQ4 How can model interpretability and generalizability be improved for real-world clinical deployment?
• RQ5 What future trends and technologies (e.g., federated learning, neuromorphic computing) could overcome current limitations in computational pathology?

Key findings

• Deep learning models, particularly CNN-based architectures, demonstrate high accuracy in classifying tumor types from whole-slide images, with some models achieving AUC > 0.95 on benchmark datasets.
• Integration of multi-omics data with histopathology images improves survival prediction accuracy compared to single-modality approaches.
• Data heterogeneity, limited annotation quality, and lack of standardization remain major challenges in model generalization across institutions.
• Hardware limitations and high computational costs hinder the deployment of complex models in routine clinical settings.
• Federated learning and privacy-preserving AI show promise in enabling collaborative model training without sharing sensitive patient data.
• Explainability techniques such as Grad-CAM help pathologists interpret model predictions, increasing trust and clinical adoption potential.