Keywords: Diagnosis/Prediction, Brain
Motivation: Accurate prediction of isocitrate dehydrogenase (IDH) mutations from multimodal MRI remains challenging due to tumor heterogeneity.
Goal(s): We proposed a Hierarchical Attention Based Multi Instance Learning (HAB-MIL) framework for preoperative IDH prediction.
Approach: This method utilizes features associated with IDH mutations and incorporates positional encoding to enhance pixel position cues. Additionally, it employs attention-based gated pooling on 3D instances to improve the understanding of labels.
Results: Our method achieved an AUC of 91.1% and accuracy of 93.7% on the TCIA dataset, outperforming state-of-the-art techniques. Finally, Grad-CAM was employed to visualize the results of the model.
Impact: The incorporation of a dynamic attention mechanism in HAB-MIL effectively explores tumor-related features for IDH prediction, while also fully leveraging tumor positional information. This enhancement improves the model's interpretability, providing more valuable support for clinical diagnosis.
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