# Computational Pathology: Challenges and Promises for Tissue Analysis

arXiv: https://arxiv.org/abs/1601.00027 · HTML: https://mlsanity.com/paper/1601.00027/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2015.

## Built on by (most cited first, 20 of 20)

- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
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- [Data Models for Dataset Drift Controls in Machine Learning With Optical Images](https://mlsanity.com/paper/2211.02578/) (2022)
- [Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology](https://mlsanity.com/paper/2509.17847/) (2025)
- [FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification](https://mlsanity.com/paper/2302.12662/) (2023)
- [Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification](https://mlsanity.com/paper/2305.02032/) (2023)
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- [Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning](https://mlsanity.com/paper/2407.17157/) (2024)
- [VLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representation](https://mlsanity.com/paper/2502.20850/) (2025)
- [Gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotations](https://mlsanity.com/paper/2211.04649/) (2022)
- [Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes](https://mlsanity.com/paper/2301.05938/) (2023)
- [Staining normalization in histopathology: Method benchmarking using multicenter dataset](https://mlsanity.com/paper/2506.19106/) (2025)
- [A Single Detect Focused YOLO Framework for Robust Mitotic Figure Detection](https://mlsanity.com/paper/2509.02637/) (2025)
- [Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Images](https://mlsanity.com/paper/2511.11486/) (2025)
- [Destroy Me: Automatic Artifact Generation for Histopathology Images](https://mlsanity.com/paper/2608.27516/) (2026)

## Abstract (arXiv)

The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.