Computational Pathology: A Survey Review and The Way Forward

TL;DR

This survey maps the end-to-end landscape of Computational Pathology (CPath), identifying the drivers and barriers to clinical adoption. It organizes the field around a data–model–application cycle and introduces model-cards to standardize cross-study comparisons, while highlighting data limitations, annotation challenges, and regulatory hurdles. The authors synthesize architectures, learning paradigms, and evaluation practices across diagnostics, prognosis, and treatment-response tasks, and emphasize the need for clinical validation and pre-/post-analytical CAD tools. They chart future directions including contrastive self-supervised learning, multi-domain and federated learning, vision-language models, and organ-diverse data collaborations to bridge the gap between research and routine clinical use.

Abstract

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).

Figures (12)

• Figure 1: We divide the data science workflow for pathology into multiple stages, wherein each brings a different level of experience. For example, the annotation/ground truth labelling stage (c) is where domain expert knowledge is consulted as to augment images with associated metadata. Meanwhile, in the evaluation phase (e), we have computer vision scientists, software developers, and pathologists working in concert to extract meaningful results and implications from the representation learning.
• Figure 2: Quality assurance and control phases developed by pathologists to oversee the clinical pathology workflow into three main phases of pre-analytical, analytical, and post-analytica phases. We further show how each of these processes can be augmented under the potential CPath applications in an end-to-end pipeline.
• Figure 3: The categorization of diagnostic tasks in computational pathology along with examples A) Detection: common detection task such as differentiating positive from negative classes like malignant from benign, B) Tissue Subtype Classification: classification task for tumorous tissue, Stroma, and adipose tissue, C) Disease Diagnosis: common disease diagnosis task like cancer staging, D) Segmentation: tumor segmentation in WSIs, and E) Prognosis tasks: shows a graph comparing survival rate and months after surgery.
• Figure 4: Distribution of diagnostic tasks in CPath for different organs from Table \ref{['OV']}. This distribution includes more than 400 cited works from 2018 to 2022 inclusive. The x-axis covers different organs, the y-axis displays different diagnostic tasks, and the height of the bars along the vertical axis measures the number of works that have examined the specific task and organ. Please refer to Table \ref{['OV']} in the supplementary section for more information.
• Figure 5: WSI tissue images with different types of histological stains. Each stain highlights different areas and structures of the tissue in order to aid in visualizing underlying characteristics. Amongst this diversity, there is Hematoxylin and Eosin or H$\&$E which is mainly used in studies as most histopathological processes can be understood from this stain. All images provided are under a Creative Commons license, specifics on the license can be found in the references.