ArticleQuantitative imaging in medicine and surgery2025
The application of multi-instance learning based on feature reconstruction and cross-mixing in the Gleason grading of prostate cancer from whole-slide images.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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11 authors.
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Abstract
Background: Prostate cancer is a common malignancy in men, requiring accurate diagnosis and prognosis. The Gleason grading system remains the preferred method of evaluation and is critical to risk stratification and informing treatment strategies. However, analyzing whole-slide image (WSI) is significantly challenging due to high pixel density, tumor heterogeneity, and the difficulty in acquiring precise annotated data. This study developed a weakly supervised multiple instance learning (MIL)-based method for Gleason grading of prostate cancer pathology images, aiming to enhance tumor classification performance and provide more reliable support for clinical risk assessment and treatment strategies. Methods: This study developed a novel feature reconstruction and cross-mixing-based MIL (FRCM-MIL) method to enhance the accuracy of prostate cancer from WSIs. This method includes a spatial feature reconstruction module based on wavelet transform (SFRM-WT), which combines frequency domain information to extract more diverse features. A cross-attention module (CAM) was included to enhance feature interaction and fusion. Additionally, a confidence query aggregation module (CQAM) was used to consolidate input features and create confidence-enhanced outputs. Results: The proposed method achieved an accuracy of 81.75% and an area under the curve (AUC) of 94.41% on the Peking Union Medical College Hospital (PUMCH) dataset, along with an accuracy of 67.24% and an AUC of 91.69% on the Prostate Cancer Grade Assessment Challenge (PANDA) dataset, outperforming existing state-of-the-art approaches. Conclusions: The FRCM-MIL model performs outstandingly in the Gleason grading task for prostate cancer WSIs, effectively distinguishing between different grades. This model has the potential to assist clinicians in formulating personalized chemotherapy and radiotherapy plans, ultimately improving treatment outcomes and demonstrating significant clinical value.
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