Fig. 3: Rigorous evaluation of model performance via ablation study. | npj Digital Medicine

Fig. 3: Rigorous evaluation of model performance via ablation study.

From: Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning

Fig. 3

a Our comprehensive ablation study assesses the three-branch multimodal GMLF against different unimodal and bimodal baseline models formed based on the three distinct feature modalities. Specifically, Neural Embeddings refers to the GNN branch using ResNet50 for patch-level feature extraction, Cell Type and Morphology to another GNN branch using HoVer-Net for patch-level feature extraction, and Gene Expression to the branch analyzing patient-level gene expression data from tissue microarrays. b The AUROC (Area Under the Receiver Operating Characteristic) performance across different modality compositions is evaluated during the 5-fold cross-validation and tested on 20% internal validation data, with models trained on the 80% discovery dataset, for predicting response to neoadjuvant chemotherapy (NAC).

Back to article page