arXiv:2005.04073
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
원문 초록
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Hybrid-View Attention for csPCa Classification in TRUS
Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding
MS-IQA: A Multi-Scale Feature Fusion Network for PET/CT Image Quality Assessment
토론
이 논문과 연결된 포럼 글이 아직 없습니다.