mlsanityData to 7 Oct 2026

AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics

arXiv 2406.14866 ↗First posted 21 Jun 2024cs.AI eess.IVcited by 8

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Abstract (arXiv)

While previous studies have demonstrated the potential of AI to diagnose diseases in imaging data, clinical implementation is still lagging behind. This is partly because AI models require training with large numbers of examples only available for common diseases. In clinical reality, however, only few diseases are common, whereas the majority of diseases are less frequent (long-tail distribution). Current AI models overlook or misclassify these diseases. We propose a deep anomaly detection approach that only requires training data from common diseases to detect also all less frequent diseases. We collected two large real-world datasets of gastrointestinal biopsies, which are prototypical of the problem. Herein, the ten most common findings account for approximately 90% of cases, whereas the remaining 10% contained 56 disease entities, including many cancers. 17 million histological images from 5,423 cases were used for training and evaluation. Without any specific training for the diseases, our best-performing model reliably detected a broad spectrum of infrequent ("anomalous") pathologies with 95.0% (stomach) and 91.0% (colon) AUROC and generalized across scanners and hospitals. By design, the proposed anomaly detection can be expected to detect any pathological alteration in the diagnostic tail of gastrointestinal biopsies, including rare primary or metastatic cancers. This study establishes the first effective clinical application of AI-based anomaly detection in histopathology that can flag anomalous cases, facilitate case prioritization, reduce missed diagnoses and enhance the general safety of AI models, thereby driving AI adoption and automation in routine diagnostics and beyond.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 46 of its 93 references that are in the map. First posted on arXiv 21 Jun 2024.

Builds on

46 of 93 references.

References · 46

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34 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignosticscited by 10
  2. 2025MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classificationcited by 6
  3. 2026Atlas 2 -- Foundation models for clinical deploymentcited by 6
  4. 2025RELICT: A Replica Detection Framework for Medical Image Generationcited by 0
  5. 2025Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Imagescited by 0
  6. 2025Value bounds and Convergence Analysis for Averages of LRP attributionscited by 0
  7. 2026Toxicity Assessment in Preclinical Histopathology via Class-Aware Mahalanobis Distance for Known and Novel Anomaliescited by 0
  8. 2026HERO: Histology Encoder for Robust Representation in Oncologycited by 0