# Computational Pathology: A Survey Review and The Way Forward

arXiv: https://arxiv.org/abs/2304.05482 · HTML: https://mlsanity.com/paper/2304.05482/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2023.

## Built on by (most cited first, 30 of 30)

- [SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding](https://mlsanity.com/paper/2410.11761/) (2024)
- [Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction](https://mlsanity.com/paper/2401.01646/) (2024)
- [2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification](https://mlsanity.com/paper/2412.00678/) (2024)
- [Vim4Path: Self-Supervised Vision Mamba for Histopathology Images](https://mlsanity.com/paper/2404.13222/) (2024)
- [PLUTO: Pathology-Universal Transformer](https://mlsanity.com/paper/2405.07905/) (2024)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [Investigating Zero-Shot Diagnostic Pathology in Vision-Language Models with Efficient Prompt Design](https://mlsanity.com/paper/2505.00134/) (2025)
- [Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modeling](https://mlsanity.com/paper/2505.17982/) (2025)
- [Adaptive Prototype Learning for Multimodal Cancer Survival Analysis](https://mlsanity.com/paper/2503.04643/) (2025)
- [Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation](https://mlsanity.com/paper/2409.18506/) (2024)
- [Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics](https://mlsanity.com/paper/2411.05959/) (2024)
- [Comparative Analysis of Diffusion Generative Models in Computational Pathology](https://mlsanity.com/paper/2411.15719/) (2024)
- [Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation](https://mlsanity.com/paper/2503.05319/) (2025)
- [Towards Computation- and Communication-efficient Computational Pathology](https://mlsanity.com/paper/2504.02628/) (2025)
- [CellMamba: Adaptive Mamba for Accurate and Efficient Cell Detection](https://mlsanity.com/paper/2512.21803/) (2025)
- [GroupMixer: Patch-based Group Convolutional Neural Network for Breast Cancer Detection from Histopathological Images](https://mlsanity.com/paper/2311.09846/) (2023)
- [BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification](https://mlsanity.com/paper/2404.11161/) (2024)
- [A Novel Approach to Linking Histology Images with DNA Methylation](https://mlsanity.com/paper/2504.05403/) (2025)
- [SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images](https://mlsanity.com/paper/2505.06710/) (2025)
- [Predict Patient Self-reported Race from Skin Histological Images](https://mlsanity.com/paper/2507.21912/) (2025)
- [Fourier Transform Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2510.15138/) (2025)
- [Structural Prognostic Event Modeling for Multimodal Cancer Survival Analysis](https://mlsanity.com/paper/2512.01116/) (2025)
- [A Lightweight Multi-Cancer Tumor Localization Framework for Deployable Digital Pathology](https://mlsanity.com/paper/2603.08844/) (2026)
- [CAFlow: Adaptive-Depth Single-Step Flow Matching for Efficient Histopathology Super-Resolution](https://mlsanity.com/paper/2603.18513/) (2026)
- [Efficient AI-Driven Multi-Section Whole Slide Image Analysis for Biochemical Recurrence Prediction in Prostate Cancer](https://mlsanity.com/paper/2603.20273/) (2026)
- [Parameter-efficient Prompt Tuning and Hierarchical Textual Guidance for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2603.21504/) (2026)
- [Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation](https://mlsanity.com/paper/2605.30716/) (2026)
- [From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology](https://mlsanity.com/paper/2608.03508/) (2026)
- [Gated Spatial Redundancy Projection for Pathology Transformer Attentions](https://mlsanity.com/paper/2608.08374/) (2026)
- [CARA: Collision-Aware Resolution Adaptation for Multiresolution Hash Encoding Based Image Fitting](https://mlsanity.com/paper/2609.18554/) (2026)

## Abstract (arXiv)

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).