Fig. 2: Heatmap illustrating the difference in validation AUC (MRNet - ImageNet) across model architectures (AlexNet, Swin Transformer, ViT) and view-modalities (sagittal, axial, coronal) for MRAs and standard MRIs. | npj Artificial Intelligence

Fig. 2: Heatmap illustrating the difference in validation AUC (MRNet - ImageNet) across model architectures (AlexNet, Swin Transformer, ViT) and view-modalities (sagittal, axial, coronal) for MRAs and standard MRIs.

From: SCOPE-MRI: Bankart lesion detection as a case study in data curation and deep learning for challenging diagnoses

Fig. 2

Positive values indicate higher performance with MRNet pretraining compared to ImageNet pretraining. Each cell represents the AUC difference for the corresponding model and view-modality pair, with results derived from the best-performing hyperparameter set for each model and view-modality. The AUC differences have been scaled by 100 for readability and are presented as percentages.

Back to article page