Summary of Ai-based Anomaly Detection For Clinical-grade Histopathological Diagnostics, by Jonas Dippel et al.
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
by Jonas Dippel, Niklas Prenißl, Julius Hense, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, Oliver Buchstab, David Horst, Maximilian Alber, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep anomaly detection approach is proposed to diagnose rare diseases in imaging data. Current AI models are limited by requiring large training datasets for common diseases, leading to misclassification or overlook of less frequent diseases. The authors collect two large real-world datasets of gastrointestinal biopsies, including 17 million histological images from 5,423 cases, and train a model that detects anomalous pathologies with high accuracy (95.0% AUROC in stomach and 91.0% AUROC in colon). This approach can flag anomalous cases, facilitate case prioritization, reduce missed diagnoses, and enhance the general safety of AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps diagnose diseases in imaging data, but current models are limited by needing large training datasets for common diseases. A new approach uses deep learning to detect rare diseases without requiring extra training data. The authors used lots of images from real-world cases to test their model, and it worked really well! This could help doctors find more diseases and make sure AI is safe and accurate. |
Keywords
* Artificial intelligence * Anomaly detection * Deep learning
