Summary of Selective Test-time Adaptation For Unsupervised Anomaly Detection Using Neural Implicit Representations, by Sameer Ambekar et al.
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations
by Sameer Ambekar, Julia A. Schnabel, Cosmin I. Bercea
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to test-time adaptation for anomaly detection (AD) in medical imaging, addressing challenges when adapting models to new clinical settings. The authors propose selective test-time adaptation that utilizes pre-trained features to adapt selectively to unseen domains without altering the source-trained model. This method employs a lightweight multi-layer perceptron for neural implicit representations and improves detection accuracy by up to 78% for enlarged ventricles and 24% for edemas in brain AD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make medical imaging models better at finding problems they haven’t seen before. It’s like teaching a model to recognize new patterns in images without changing what it knows already. The researchers came up with a clever way to do this using deep learning techniques and improved the accuracy of detecting things like big brain ventricles and fluid buildup. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning
