Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound
Journal:
arXiv
Published Date:
Jul 2, 2025
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.
