Fig. 3: The prediction probability distribution for different classes and saliency map visualization.

a Based on the anatomies of each case, we split them into positive cases, intra-negative cases which are located in the same anatomy as the positive ones, and inter-negative cases which are located in other anatomies. The classification threshold score in the figure denotes the final comparison bar to transform the soft probabilities into binary true/false diagnosis results. The first three probability distribution figures depict the distributions of three relatively successful classes, where the model can clearly distinguish the inter-negative cases and the intra-negative cases are more confusing. We then show two ordinary classes. As shown by the distributions, most errors are caused by the intra-negative cases, and similarly, the inter-negative cases are easily dismissed as well. At last, we show a failure case where the model can hardly distinguish the positive and negative cases regardless of whether they are intra-negative or inter-negative. b Saliency map of the key frames. Red indicates the areas that the model focuses on when inferring the corresponding disorder category. This indicates that RadDiag is capable of accurately identifying the locations of lesions or abnormal regions. Detailed abnormalities and corresponding data source links are provided in Supplementary Section G. Source data are provided as a Source Data file.