Abstract
Placenta Accreta Spectrum (PAS) can lead to high risks like excessive blood loss at the delivery. Therefore, prenatal screening with MRI is essential for delivery planning that ensures better clinical outcomes. For computer-aided PAS diagnosis, existing work mostly extracts radiomics features directly from ROI while ignoring the context information, or learns global semantic features with limited awareness of the focal area. Moreover, they usually select single or few MRI slices to represent the whole sequences, which can result in biased decisions. To deal with these issues, a novel end-to-end Hierarchical Attention and Contrastive Learning Network (HACL-Net) is proposed under the formulation of a multi-instance problem. Slice-level attention module is first designed to extract context-aware deep semantic features. These slice-wise features are then aggregated via the patient-level attention module into task-specific patient-wise representation for PAS prediction. A plug-and-play contrastive learning module is introduced to further improve the discriminating power of extracted features. Extensive experiments with ablation studies on a real clinical dataset show that HACL-Net can achieve state-of-the-art prediction performance with the effectiveness of each module.
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Acknowledgement
This research was supported by Dr. Hao Zhu’s team from Obstetrics and Gynecology Hospital affiliated to Fudan University in China.
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Lu, M., Wang, T., Zhu, H., Li, M. (2023). HACL-Net: Hierarchical Attention and Contrastive Learning Network for MRI-Based Placenta Accreta Spectrum Diagnosis. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_29
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