Multi‐Task Recurrent Convolutional Network for Rib Fracture Healing Period Prediction

Rib fracture CT image sequences present challenges such as small fracture regions, subtle inter‐class differences and complex temporal changes during healing. In this work, we propose a multi‐task recurrent convolutional network (MRCN) for simultaneous rib fracture healing period prediction and fracture type classification. The method uses a shared feature encoder to extract semantic features from CT images and two task‐specific branches to model healing status and fracture type information. In the prediction branch, a CE‐LSTM module with contextual expression is introduced to capture temporal dependencies and contextual information across CT image sequences. In the classification branch, Bi‐VA and CAM modules are used for multi‐scale feature fusion and fracture‐region enhancement. A joint optimisation strategy enables feature sharing and complementary learning between the two tasks. Experiments on the rib fracture dataset from Shanxi Bethune Hospital show that MRCN achieves 93.20% accuracy for healing period prediction and 87.90% accuracy for fracture type classification, showing better performance than the compared methods.