mlsanityData to 14 Oct 2026

MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transport

arXiv 2507.06733 ↗First posted 9 Jul 2025cs.CVcited by 2

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Abstract (arXiv)

Medical anomaly detection (AD) is challenging due to diverse imaging modalities, anatomical variations, and limited labeled data. We propose a novel approach combining visual adapters and prompt learning with Partial Optimal Transport (POT) and contrastive learning (CL) to improve CLIP's adaptability to medical images, particularly for AD. Unlike standard prompt learning, which often yields a single representation, our method employs multiple prompts aligned with local features via POT to capture subtle abnormalities. CL further enforces intra-class cohesion and inter-class separation. Our method achieves state-of-the-art results in few-shot, zero-shot, and cross-dataset scenarios without synthetic data or memory banks. The code is available at https://github.com/mahshid1998/MADPOT.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

23 of 40 references.

References · 23

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  4. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  6. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  7. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  8. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  9. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  10. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  11. 2015Learning with a Wasserstein Losscited by 196
  12. 2022Registration based Few-Shot Anomaly Detectioncited by 133
11 more
  1. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 127
  2. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  3. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  4. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  5. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  6. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 46
  7. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
  8. 2023OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methodscited by 28
  9. 2022Optimal transport for causal discoverycited by 19
  10. 2023CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labelscited by 17
  11. 2024Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transportcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQAcited by 0
  2. 2026SLAP: Selective Local Vision-Language Alignment for Fish Re-Identification via Partial Optimal Transportcited by 0