Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Deep learning (DL)-based pathological image modelling and analysis approaches offer transformative potential for early cancer diagnostics, yet limited sample sizes and a lack of interpretability often hinder efficient clinical translation. Here, we present the interpretable Multi-Task Digital Pathology Model (iMDPath), an end-to-end, highly explainable multi-task deep learning framework that simul...
Proper monitoring of tumor progression and evaluation of treatment responses highly depend on longitudinal brain tumor segmentation from MRI data. Current deep learning methodologies have mainly concentrated on analyzing single-time-point images, which restricts the ability to incorporate temporal dynamics during the segmentation process. The study proposes a new approach called Temporal-Spatial T...
The lack of validated stage-specific biomarkers hampers the understanding of Alzheimer's disease (AD) progression and clinical translation. Current tr...
Classification of brain tumors is a difficult problem in medical imaging analysis. Over the past few years, various deep learning-based techniques hav...
The classification of immunophenotypes in muscle-invasive bladder cancer (MIBC) is critical for predicting immunotherapy response and clinical outcome...
BACKGROUND: Lymph node metastasis (LNM) is an important prognostic factor but is often underdiagnosed due to limitations in conventional assessment me...
Early and accurate diagnosis of breast cancer is critical for minimizing needle biopsies and enhancing patient outcomes and requires effective integra...
BACKGROUND: Perforation following chemotherapy in gastrointestinal lymphoma (PFCGL) is a rare but severe and life-threatening complication. Early pre-...
BACKGROUND/AIMS: Human epidermal growth factor receptor 2 (HER2) overexpression is a critical therapeutic target in gallbladder cancer (GBC), but dete...
BACKGROUND: Neoadjuvant chemotherapy (NAC) is a critical therapeutic strategy for locally advanced breast cancer; however, its clinical utility is con...
Chemotherapy-induced cardiotoxicity (CIC) is a leading cause of morbidity in cancer survivors, as conventional surveillance often detects cardiac dysf...
OBJECTIVE: To systematically evaluate the diagnostic performance of artificial intelligence (AI) models for hepatocellular carcinoma (HCC) and to pool...
Immunotherapy has transformed cancer treatment but remains ineffective in many solid tumors, largely due to the immunosuppressive tumor microenvironme...
Accurate quantification of components with spectral imaging (e.g., hyperspectral and Raman) is fundamentally challenged by nonlinear mixing effects. T...
PURPOSE: Nuclear emergency medical rescue is a critical component of the nuclear emergency response system, playing a vital role in safeguarding publi...
PURPOSE: Predictive biomarkers of response to immune checkpoint inhibitors (ICI) remain poorly defined in patients with non-small cell lung cancer (NS...
BACKGROUND & OBJECTIVE: Glioblastoma Multiforme (GBM) is an aggressive and highly heterogeneous brain tumor with poor survival outcomes. While convent...
Pancreatic cancer remains a formidable global health challenge, ranking as the twelfth most common malignancy yet claiming an outsized toll with disma...
BACKGROUND: Conventional age-based breast cancer screening ignores substantial inter-individual risk variation, contributing to overdiagnosis, false p...
Nanomedicine offers powerful opportunities to overcome the pharmacokinetic and microenvironmental limitations of conventional chemotherapy, particular...