MAG-Net: A Multi-Task Deep Learning Framework for Thymic Tumor Diagnosis

Abstract

Automatic segmentation and classification of thymic tumors based on preoperative CT scans are critical for clinical diagnosis. However, significant variability in the shape, size, and texture of thymic tumors, along with their blurred boundaries and complex pathological features, poses substantial challenges to automated recognition. This study focuses on two key objectives: (1) semantic segmentation at the pixel level of thymic tumors on CT images and (2) identification of high-risk thymic carcinoma. To address the above challenges, we propose a multiview attention-guided network (MAG-Net), a novel multitask learning framework guided by attention mechanisms. The model simultaneously takes 3D subvolumes of equal size extracted from axial, coronal, and sagittal views of the CT scan as input and fuses features across multiple views under the guidance of attention mechanisms. Moreover, we introduce a segmentation-classification prior attention (SCPA) module that embeds spatial location cues from segmentation into feature learning for classification. Extensive experiments conducted on both our own collected data set and public data sets demonstrate the effectiveness of the proposed method, achieving a dice coefficient of 90.54% for the segmentation task and an AUC of nearly 0.9 for classification. To facilitate further research, the analysis code is available on https://github.com/weixuxuxu/MAGNet.

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