Description
Book SynopsisClassification and characterization.- Multi-center ovarian tumor classification using hierarchical transformer-based multiple-instance learning.- FoTNet Enables Preoperative Differentiation of Malignant Brain Tumors with Deep Learning.- Classification of Endoscopy and Video Capsule Images using Hybrid Model.- Multimodal Deep Learning-based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases.- Seeing More with Less: Meta-Learning and Diffusion Models for Tumor Characterization in Low-data Settings.- Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification.- Detection and Segmentation.- On undesired emergent behaviors in compound prostate cancer detection systems.- Optimizing Multi-Expert Consensus for Classification and Precise Localization of Barrett's Neoplasia.- Automated Hepatocellular Carcinoma Analysis in Multi-Phase CT with Deep Learning.- Refining deep learning segmentation maps with a local thresholding approach: application to liver surface nodularity quantification in CT.- Uncertainty-Aware Deep Learning Classification for MRI-based Prostate Cancer Detection.- Generalized Polyp Detection from Colonoscopy frames Using proposed EDF-YOLO8 Network.- AI-Assisted Laryngeal Examination System.- UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision.- SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling.- Cancer/Early cancer detection, treatment, and survival prognosis.-AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients.- Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery.- Treatment efficacy prediction of focused ultrasound therapies using multi-parametric magnetic resonance imaging.- SurRecNet: A Multi-Task Model with Integrating MRI and Diagnostic Descriptions for Rectal Cancer Survival Analysis.- Improved prediction of recurrence after prostate cancer radiotherapy using multimodal data and in silico simulations.- AutoDoseRank: Automated Dosimetry-informed Segmentation Ranking for Radiotherapy.- SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network.
