A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging
Journal:
arXiv
Published Date:
Feb 27, 2025
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.
