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.

翻译：皮肤病是最普遍的健康问题之一，准确的计算机辅助诊断方法对皮肤科医生和患者都至关重要。然而，现有方法大多忽略了皮肤病诊断所需的关键领域知识。为此，提出了一种名为DermImitFormer的新型多任务模型，通过模仿皮肤科医生的诊断流程与策略来填补这一空白。通过多任务学习，该模型同时预测身体部位、病变属性以及疾病本身，既提升了诊断精度，又增强了诊断可解释性。设计的病灶选择模块模仿了皮肤科医生的放大动作，能有效从噪声背景中突出局部病灶特征。此外，提出的交叉交互模块显式地建模了身体部位、病变属性与疾病之间复杂的诊断推理关系。为更稳健地评估该方法，建立了一个比现有数据集病例数量显著更多的大规模皮肤病临床图像数据集。在三个不同数据集上的大量实验一致表明，所提方法达到了最先进的识别性能。