DNP-Guided Contrastive Reconstruction with a Reverse Distillation Transformer for Medical Anomaly Detection
This work solves the problem of accurate anomaly detection for medical imaging applications, offering an incremental advance by combining trainable encoders with prototype-guided reconstruction to enhance domain adaptation and prevent prototype collapse.
The paper tackles the problem of anomaly detection in medical images by addressing prototype collapse in prototype-based learning and limited adaptation of pre-trained encoders, resulting in significant improvements in representation quality and anomaly localization that outperform prior methods on multiple benchmarks.
Anomaly detection in medical images is challenging due to limited annotations and a domain gap compared to natural images. Existing reconstruction methods often rely on frozen pre-trained encoders, which limits adaptation to domain-specific features and reduces localization accuracy. Prototype-based learning offers interpretability and clustering benefits but suffers from prototype collapse, where few prototypes dominate training, harming diversity and generalization. To address this, we propose a unified framework combining a trainable encoder with prototype-guided reconstruction and a novel Diversity-Aware Alignment Loss. The trainable encoder, enhanced by a momentum branch, enables stable domain-adaptive feature learning. A lightweight Prototype Extractor mines informative normal prototypes to guide the decoder via attention for precise reconstruction. Our loss enforces balanced prototype use through diversity constraints and per-prototype normalization, effectively preventing collapse. Experiments on multiple medical imaging benchmarks show significant improvements in representation quality and anomaly localization, outperforming prior methods. Visualizations and prototype assignment analyses further validate the effectiveness of our anti-collapse mechanism and enhanced interpretability.