[Attention-based multi-task deep learning model for predicting the primary site of cervical metastatic squamous cell carcinoma with unknown primary].

Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma

Predicting response to induction chemotherapy (IC) and overall survival (OS) is critical for optimizing treatment in patients with locally advanced nasopharyngeal carcinoma (LANPC). This study aimed to develop and validate a multi-task deep learning model integrating pretreatment MRI and whole slide images (WSIs) to pr...

Cross-phase attention-based multi-phase CT deep learning for preoperative risk stratification of gastric gastrointestinal stromal tumors in a multicenter study.

PURPOSE To develop and externally validate a multi-phase CT deep learning framework for preoperative risk stratification of 2-5 cm gastric gastrointestinal stromal tumors (GISTs). METHODS This retrospective multicenter study included 427 patients with pathologically confirmed gastric GISTs. At Center A, patients trea...

Multimodal Transformer Integrating CT and Pathology for Postoperative Risk Stratification in Stage I Non-Small Cell Lung Cancer.

The DLRTransformer improved postoperative risk stratification compared with guideline-based risk stratification and identified patients with stage I NSCLC who benefited from adjuvant therapy.

A multi-stage multi-level attention-based feature learning framework for multi-endpoint survival prediction in head and neck cancer

MRI-Based 2.5D Multi-Instance Deep Learning for Differentiating Pathological Subtypes of Brain Metastases From Lung Cancer.

BACKGROUND Distinct treatment strategies for small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) brain metastases require accurate noninvasive subtyping. PURPOSE To develop and externally validate a biparametric MRI-based 2.5D multi-instance learning (MIL) deep learning (DL) model for discriminating...

A comprehensive review of attention-based multimodal deep learning techniques for lung cancer diagnosis

Lung cancer remains the deadliest cancer type worldwide, presenting a wide spectrum of subtypes, including several rare and difficult-to-diagnose types. Improving diagnostic accuracy and efficiency with technology seems promising, which helps medical experts to save many patients’ lives. Deep learning has emerged as a...