mlsanityData to 7 Oct 2026

Computational Pathology: Challenges and Promises for Tissue Analysis

arXiv 1601.00027 ↗First posted 31 Dec 2015cs.CV cs.AIcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2015.

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Citing papers per year: 2022: 5; 2023: 5; 2024: 1; 2025: 7; 2026: 2.

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  1. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 86
  2. 2022A graph-transformer for whole slide image classificationcited by 84
  3. 2022Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutationcited by 7
  4. 2023MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysiscited by 6
  5. 2022Data Models for Dataset Drift Controls in Machine Learning With Optical Imagescited by 5
  6. 2023FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classificationcited by 3
  7. 2023Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classificationcited by 3
  8. 2025Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathologycited by 3
  9. 2026MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understandingcited by 3
  10. 2022Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformercited by 2
  11. 2025A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosiscited by 2
  12. 2025Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Modelcited by 2
8 more
  1. 2024Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learningcited by 1
  2. 2025VLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representationcited by 1
  3. 2022Gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotationscited by 0
  4. 2023Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodescited by 0
  5. 2025Staining normalization in histopathology: Method benchmarking using multicenter datasetcited by 0
  6. 2025A Single Detect Focused YOLO Framework for Robust Mitotic Figure Detectioncited by 0
  7. 2025Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Imagescited by 0
  8. 2026Destroy Me: Automatic Artifact Generation for Histopathology Imagescited by 0