PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection

• URL: http://arxiv.org/abs/2603.07142v1
• Date: Sat, 07 Mar 2026 10:25:39 GMT
• Title: PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection
• Abstract summary: We propose PDD (Manifold-Prior Diverse Distillation), a framework that unifies dual-teacher priors into a shared high-dimensional manifold.<n>Experiments on multiple medical datasets demonstrate that PDD significantly outperforms existing state-of-the-art methods.
• Score: 34.22949331554283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image anomaly detection faces unique challenges due to subtle, heterogeneous anomalies embedded in complex anatomical structures. Through systematic Grad-CAM analysis, we reveal that discriminative activation maps fail on medical data, unlike their success on industrial datasets, motivating the need for manifold-level modeling. We propose PDD (Manifold-Prior Diverse Distillation), a framework that unifies dual-teacher priors into a shared high-dimensional manifold and distills this knowledge into dual students with complementary behaviors. Specifically, frozen VMamba-Tiny and wide-ResNet50 encoders provide global contextual and local structural priors, respectively. Their features are unified through a Manifold Matching and Unification (MMU) module, while an Inter-Level Feature Adaption (InA) module enriches intermediate representations. The unified manifold is distilled into two students: one performs layer-wise distillation via InA for local consistency, while the other receives skip-projected representations through a Manifold Prior Affine (MPA) module to capture cross-layer dependencies. A diversity loss prevents representation collapse while maintaining detection sensitivity. Extensive experiments on multiple medical datasets demonstrate that PDD significantly outperforms existing state-of-the-art methods, achieving improvements of up to 11.8%, 5.1%, and 8.5% in AUROC on HeadCT, BrainMRI, and ZhangLab datasets, respectively, and 3.4% in F1 max on the Uni-Medical dataset, establishing new state-of-the-art performance in medical image anomaly detection. The implementation will be released at https://github.com/OxygenLu/PDD

Related papers

• Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement [0.0]
  Anomaly detection in medical imaging is essential for identifying rare pathological conditions.<n>We propose a hybrid anomaly detection framework that integrates self-supervised representation learning with manifold-based density estimation.
  arXiv  Detail & Related papers  (2026-04-21T07:58:44Z)
• Using Unsupervised Domain Adaptation Semantic Segmentation for Pulmonary Embolism Detection in Computed Tomography Pulmonary Angiogram (CTPA) Images [0.0]
  unsupervised domain adaptation (UDA) framework is proposed, utilizing a Transformer backbone and a Mean-Teacher architecture for cross-center semantic segmentation.<n>The primary focus is placed on enhancing pseudo-label reliability by learning deep structural information within the feature space.<n> Experimental validation conducted on cross-center datasets (FUMPE and CAD-PE) demonstrates significant performance gains.
  arXiv  Detail & Related papers  (2026-02-23T14:33:24Z)
• Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications [1.192436948211501]
  We propose Multi-AD, a convolutional neural network (CNN) model for robust unsupervised anomaly detection across medical and industrial images.<n>Our approach employs the squeeze-and-excitation (SE) block to enhance feature extraction via channel-wise attention.<n>At the inference stage, by integrating multi-scale features, the student model can detect anomalies of varying sizes.
  arXiv  Detail & Related papers  (2026-02-05T08:17:42Z)
• Multimodal Visual Surrogate Compression for Alzheimer's Disease Classification [69.87877580725768]
  Multimodal Visual Surrogate Compression (MVSC) learns to compress and adapt large 3D sMRI volumes into compact 2D features.<n>MVSC has two key components: a Volume Context that captures global cross-slice context under textual guidance, and an Adaptive Slice Fusion module that aggregates slice-level information in a text-enhanced, patch-wise manner.
  arXiv  Detail & Related papers  (2026-01-29T13:05:46Z)
• A Semantic Segmentation Algorithm for Pleural Effusion Based on DBIF-AUNet [22.657295396752023]
  Pleural effusion semantic segmentation can significantly enhance the accuracy and timeliness of clinical diagnosis and treatment.<n>Existing methods often struggle with diverse image variations and complex edges.<n>We propose the Dual-Branch Interactive Fusion Attention model (DBIF-AUNet) to address these challenges.
  arXiv  Detail & Related papers  (2025-08-08T10:14:51Z)
• OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection [1.0190194769786831]
  Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data.<n>We propose a novel method that tightly couples representation learning with an analytically solvable one-class SVM.<n>The model is evaluated on two tasks: a new benchmark based on MNIST-C, and a challenging brain MRI subtle lesion detection task.
  arXiv  Detail & Related papers  (2025-07-25T13:00:40Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer [53.575573940055335]
  We propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities.
  arXiv  Detail & Related papers  (2023-01-19T03:42:36Z)
• ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection [75.68023968735523]
  Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions. We propose an innovative approach called Asymmetric Distillation Post-Segmentation (ADPS) Our ADPS employs an asymmetric distillation paradigm that takes distinct forms of the same image as the input of the teacher-student networks. We show that ADPS significantly improves Average Precision (AP) metric by 9% and 20% on the MVTec AD and KolektorSDD2 datasets.
  arXiv  Detail & Related papers  (2022-10-19T12:04:47Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.