IEEE

Tackling Tumor Heterogeneity Issue: Transformer-Based Multiple Instance Enhancement Learning for Predicting EGFR Mutation via CT Images

Abstract: Accurate and non-invasive prediction of epidermal growth factor receptor (EGFR) mutation is crucial for the diagnosis and treatment of non-small cell lung cancer (NSCLC). While computed ...
GitHub

Rethinking Multi-Instance Learning through Graph-Driven Fusion: A Dual-Path Approach to Adaptive Representation

GDF-MIL (Graph-Driven Fusion Multiple Instance Learning) is a novel graph-driven multi-instance learning framework that adaptively balances topology modeling and semantic feature preservation through ...
IEEE

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

Abstract: Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular ...
GitHub

kikaiTollenge/instance-based-self-adaptive-scale-fusion

This is an instance-based self-adaptive scale fusion implementation built on PyTorch. In ./model/adaptiveScaleFusion.py, hybrid weights are assigned across two instance scales, and the method has been ...