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ARXIV:2604.19191 · MEDICAL AI · SUBMITTED 22 APR · 02:14 UTC · FRESHNESS STALE
ARXIV:2604.19191MEDICAL AISUBMITTED 22 APR · 02:14 UTCFRESHNESS STALEPritam Kar · Gouri Lakshmi S · Saptarshi Bej · arXiv
A hybrid anomaly detection framework for medical imaging that enhances self-supervised learning with manifold-based density estimation to achieve state-of-the-art performance in early disease detection.
Opportunity summary
Pain A hybrid anomaly detection framework for medical imaging that enhances self-supervised learning with manifold-based density estimation to achieve state-of-the-art performance in early disease detection.
Evidence 0 refs | 3 sources | 50% coverage
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A hybrid anomaly detection framework for medical imaging that enhances self-supervised learning with manifold-based density estimation to achieve state-of-the-art performance in early disease detection. We propose a hybrid anomaly detection framework that integrates self-supervised…
Anomaly detection in medical imaging is essential for identifying rare pathological conditions, particularly when annotated abnormal samples are limited. We propose a hybrid anomaly detection framework that integrates self-supervised representation learning with manifold-based density…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on seven medical imaging datasets demonstrate state-of-the-art performance. Code availability is flagged in the production record; the public repository link still needs…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A hybrid anomaly detection framework for medical imaging that enhances self-supervised learning with manifold-based density estimation to achieve state-of-the-art performance in early disease detection.
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10.48550/arXiv.2604.19191A hybrid anomaly detection framework for medical imaging that enhances self-supervised learning with manifold-based density estimation to achieve state-of-the-art performance in early disease detection.
Abstract
Anomaly detection in medical imaging is essential for identifying rare pathological conditions, particularly when annotated abnormal samples are limited. We propose a hybrid anomaly detection framework that integrates self-supervised representation learning with manifold-based density estimation, a combination that remains largely unexplored in this domain. Medical images are first embedded into a latent feature space using pretrained, potentially domain-specific, backbones. These representations are then refined via Mean Shift Density Enhancement (MSDE), an iterative manifold-shifting procedure that moves samples toward regions of higher likelihood. Anomaly scores are subsequently computed using Gaussian density estimation in a PCA-reduced latent space, where Mahalanobis distance measures deviation from the learned normal distribution. The framework follows a one-class learning paradigm and requires only normal samples for training. Extensive experiments on seven medical imaging datasets demonstrate state-of-the-art performance. MSDE achieves the highest AUC on four datasets and the highest Average Precision on five datasets, including near-perfect performance on brain tumor detection (0.981 AUC/AP). These results underscore the potential of the proposed framework as a scalable clinical decision-support tool for early disease detection, screening in low-label settings, and robust deployment across diverse imaging modalities.
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PROBLEM
A hybrid anomaly detection framework for medical imaging that enhances self-supervised learning with manifold-based density estimation to achieve state-of-the-art performance in early disease detection. We propose a hybrid anomaly detection framework that integrates self-supervi...
METHOD
Anomaly detection in medical imaging is essential for identifying rare pathological conditions, particularly when annotated abnormal samples are limited. We propose a hybrid anomaly detection framework that integrates self-supervised representation learning with manifold-based d...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on seven medical imaging datasets demonstrate state-of-the-art performance. Code availability is flagged in the production record; the public repository link still needs proof alignm...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 21, "author": "Pritam Kar; Gouri Lakshmi S; Saptarshi Bej", "title": "Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement", "creation date": null
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A hybrid anomaly detection framework for medical imaging that enhances self-supervised learning with manifold-based density estimation to achieve state-of-the-art performance in early disease detection.
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