# A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging Source: [A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:11:54.790Z Authors: Junji Lin, Yi Zhang, Yunyue Pan, Yuli Chen, Chengchang Pan, Honggang Qi Published: 2025-02-27T02:02:59.000Z ## Abstract Detection and classification of pulmonary nodules is a challenge in medical image analysis due to the variety of shapes and sizes of nodules and their high concealment. Despite the success of traditional deep learning methods in image classification, deep networks still struggle to perfectly capture subtle changes in lung nodule detection. Therefore, we propose a residual multi-task network (Res-MTNet) model, which combines multi-task learning and residual learning, and improves feature representation ability by sharing feature extraction layer and introducing residual connections. Multi-task learning enables the model to handle multiple tasks simultaneously, while the residual module solves the problem of disappearing gradients, ensuring stable training of deeper networks and facilitating information sharing between tasks. Res-MTNet enhances the robustness and accuracy of the model, providing a more reliable lung nodule analysis tool for clinical medicine and telemedicine. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()