  The 7-point checklist (7PCL) is widely used in dermoscopy to identify
malignant melanoma lesions needing urgent medical attention. It assigns point
values to seven attributes: major attributes are worth two points each, and
minor ones are worth one point each. A total score of three or higher prompts
further evaluation, often including a biopsy. However, a significant limitation
of current methods is the uniform weighting of attributes, which leads to
imprecision and neglects their interconnections. Previous deep learning studies
have treated the prediction of each attribute with the same importance as
predicting melanoma, which fails to recognize the clinical significance of the
attributes for melanoma. To address these limitations, we introduce a novel
diagnostic method that integrates two innovative elements: a Clinical
Knowledge-Based Topological Graph (CKTG) and a Gradient Diagnostic Strategy
with Data-Driven Weighting Standards (GD-DDW). The CKTG integrates 7PCL
attributes with diagnostic information, revealing both internal and external
associations. By employing adaptive receptive domains and weighted edges, we
establish connections among melanoma's relevant features. Concurrently, GD-DDW
emulates dermatologists' diagnostic processes, who first observe the visual
characteristics associated with melanoma and then make predictions. Our model
uses two imaging modalities for the same lesion, ensuring comprehensive feature
acquisition. Our method shows outstanding performance in predicting malignant
melanoma and its features, achieving an average AUC value of 85%. This was
validated on the EDRA dataset, the largest publicly available dataset for the
7-point checklist algorithm. Specifically, the integrated weighting system can
provide clinicians with valuable data-driven benchmarks for their evaluations.
