ADAPTIVE MULTI-MODAL DEEP LEARNING WITH INTELLIGENT WATER DROPS OPTIMIZATION AND MULTIPLE INSTANCE LEARNING FOR ESOPHAGEAL CANCER DIAGNOSTICS

Abstract

The diagnosis of esophageal cancer needs to be done accurately and reliably, which requires the use of different clinical data, such as radiological imaging, genomic information, and endoscopic findings. An innovative multi-modal deep learning framework is newly introduced for the Intelligent Water Drops (IWD) optimization, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) with Multiple Instance Learning (MIL) are used to handle the spatial tissue heterogeneity, and fully connected (FC) fusion layers are added. A high area under the receiver operating characteristic curve (AUC-ROC) of 99.5% and an overall accuracy of 99.1% are obtained from extensive evaluation using rigorous nested cross-validation on the TCIA, TCGA-ESCA, and BE2021 datasets, confirming high diagnostic performance. To guarantee clinical reliability and reduce the risk of false confidence, clinical interpretation of uncertainty estimation and explainability analysis methods are included: Monte Carlo Dropout, Bayesian ensembles, Grad-CAM and attention maps. Comparing to multi-modal baselines indicates statistically significant enhancements on recall and AUC-ROC (p< 0.05), especially with respect to the classification of diagnostic cases that are complex and ambiguous. This framework adaptively adjusts hyperparameter settings and modality fusion weights to improve multimodal cancer diagnosis while maintaining interpretability, thereby enhancing its relevance for precision oncology.