A Novel Multi-Task Model Imitating Dermatologists for Accurate
  Differential Diagnosis of Skin Diseases in Clinical Images
Yan-Jie Zhou Wei Liu Yuan Gao Jing Xu Le Lu Yuping Duan Hao Cheng Na Jin Xiaoyong Man Shuang Zhao Yu Wang
Skin diseases are among the most prevalent health issues, and accurate
computer-aided diagnosis methods are of importance for both dermatologists and
patients. However, most of the existing methods overlook the essential domain
knowledge required for skin disease diagnosis. A novel multi-task model, namely
DermImitFormer, is proposed to fill this gap by imitating dermatologists'
diagnostic procedures and strategies. Through multi-task learning, the model
simultaneously predicts body parts and lesion attributes in addition to the
disease itself, enhancing diagnosis accuracy and improving diagnosis
interpretability. The designed lesion selection module mimics dermatologists'
zoom-in action, effectively highlighting the local lesion features from noisy
backgrounds. Additionally, the presented cross-interaction module explicitly
models the complicated diagnostic reasoning between body parts, lesion
attributes, and diseases. To provide a more robust evaluation of the proposed
method, a large-scale clinical image dataset of skin diseases with
significantly more cases than existing datasets has been established. Extensive
experiments on three different datasets consistently demonstrate the
state-of-the-art recognition performance of the proposed approach.