Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound

• URL: http://arxiv.org/abs/2507.01401v1
• Date: Wed, 02 Jul 2025 06:31:26 GMT
• Title: Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound
• Authors: Huanwen Liang, Jingxian Xu, Yuanji Zhang, Yuhao Huang, Yuhan Zhang, Xin Yang, Ran Li, Xuedong Deng, Yanjun Liu, Guowei Tao, Yun Wu, Sheng Zhao, Xinru Gao, Dong Ni,
• Abstract summary: We develop a case-level multiple instance learning (MIL)-based method for classifying fetal abdominal anomalies in prenatal ultrasound.<n>Our contribution is three-fold. First, we adopt a mixture-of-attention-experts module (MoAE) to weight different attention heads for various planes.<n> Secondly, we propose a medical-knowledge-driven feature selection module (MFS) to align image features with medical knowledge.
• Score: 28.27148137256845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fetal abdominal malformations are serious congenital anomalies that require accurate diagnosis to guide pregnancy management and reduce mortality. Although AI has demonstrated significant potential in medical diagnosis, its application to prenatal abdominal anomalies remains limited. Most existing studies focus on image-level classification and rely on standard plane localization, placing less emphasis on case-level diagnosis. In this paper, we develop a case-level multiple instance learning (MIL)-based method, free of standard plane localization, for classifying fetal abdominal anomalies in prenatal ultrasound. Our contribution is three-fold. First, we adopt a mixture-of-attention-experts module (MoAE) to weight different attention heads for various planes. Secondly, we propose a medical-knowledge-driven feature selection module (MFS) to align image features with medical knowledge, performing self-supervised image token selection at the case-level. Finally, we propose a prompt-based prototype learning (PPL) to enhance the MFS. Extensively validated on a large prenatal abdominal ultrasound dataset containing 2,419 cases, with a total of 24,748 images and 6 categories, our proposed method outperforms the state-of-the-art competitors. Codes are available at:https://github.com/LL-AC/AAcls.

Related papers

• SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images [20.315592081817595]
  We introduce SD-MAD, a two-stage Sign-Driven few-shot Multi-Anomaly Detection framework.<n>We propose three protocols to comprehensively quantify the performance of SD-MAD.
  arXiv  Detail & Related papers  (2025-05-22T13:24:37Z)
• FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesis [16.640351512021017]
  We introduce a Flexible Fetal US image generation framework (FetalFlex) to address these challenges.<n>FetalFlex incorporates a pre-alignment module to enhance controllability and introduces a repaint strategy to ensure consistent texture and appearance.<n> Experiments on multi-center datasets demonstrate that FetalFlex achieved state-of-the-art performance across multiple image quality metrics.
  arXiv  Detail & Related papers  (2025-03-19T05:16:19Z)
• Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification [50.899861205016265]
  We propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification.<n>Our framework introduces two key components into the common MIL model architecture.<n>We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets.
  arXiv  Detail & Related papers  (2025-03-08T04:51:58Z)
• Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback [57.98393950821579]
  We propose a novel UMed-LVLM designed to unveil medical abnormalities.<n>We propose a prompt method utilizing the GPT-4V to generate diagnoses based on identified abnormal areas in medical images.<n>Our UMed-LVLM significantly outperforms existing Med-LVLMs in identifying and understanding medical abnormalities.
  arXiv  Detail & Related papers  (2025-01-02T17:37:20Z)
• Measuring proximity to standard planes during fetal brain ultrasound scanning [8.328549443700858]
  This paper introduces a novel pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use. We propose a semi-supervised segmentation model utilizing both labeled SPs and unlabeled 3D US volume slices. Our model enables reliable segmentation across a diverse set of fetal brain images.
  arXiv  Detail & Related papers  (2024-04-10T16:04:21Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound [56.631021151764955]
  Standard plane recognition plays an important role in prenatal ultrasound (US) screening. We build a novel multi-label learning scheme to identify multiple standard planes and corresponding anatomical structures simultaneously.
  arXiv  Detail & Related papers  (2021-08-11T06:39:26Z)
• Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps [18.37280146564769]
  We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease.
  arXiv  Detail & Related papers  (2021-07-06T14:31:19Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)

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.