Latest AI and machine learning research in skin cancer for healthcare professionals.
One of the dominant paradigms in self-supervised learning (SSL), illustrated by MoCo or DINO, aims to produce robust representations by capturing features that are insensitive to certain image transformations such as illumination, or geometric changes. This strategy is appropriate when the objective is to recognize objects independently of their appearance. However, it becomes counterproductive as...
Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explainable artificial intelligence (xAI) capabilities required for clinical adoption. Despite achieving radiologist-level performance in tasks from whole-slide image (WSI) classification to mammographic screening, these models function as black boxes: clin...
The interaction between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlies antigen-specific T cell immunity. Despite sub...
Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, ...
Reliable, minimally invasive biomarkers for predicting immunotherapy response in head and neck squamous cell carcinoma (HNSCC) remain an unmet clinica...
Background: Interferon-gamma (IFN-{gamma}) is the primary effector cytokine of adaptive anti-tumor immunity, yet it paradoxically induces a potent imm...
Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-in...
Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using ...
Convolutional Neural Networks have shown promising effectiveness in identifying different types of cancer from radiographs. However, the opaque nature...
Recombinant human Interleukin-2 (rhIL-2, Aldesleukin) is used in immunotherapy for metastatic melanoma and renal cell carcinoma. Low-dose IL-2 has bee...
Identifying predictive biomarkers of immunotherapeutic response in melanoma remains an outstanding challenge. Existing transcriptomic and proteomic pr...
The T cell's ability to discern self and non-self depends on its T cell receptor (TCR), which recognizes peptides presented by MHC molecules. Understa...
Background: Previous research has shown that radiomics-based machine learning models are promising precision medicine tools for lesion-level predictio...
Accurate localization of tumor regions from hematoxylin and eosin-stained whole-slide images is fundamental for translational research including spati...
Clear cell renal cell carcinoma (ccRCC) is the leading cause of kidney cancer-related death, but how the tumor microenvironment shapes patient surviva...
The growing number of spatial transcriptomics (ST) datasets enables comprehensive multi-modal characterization of cell types across diverse biological...
Background: N4-acetylcytidine (ac4C) modification plays a critical role in cancer development. Exploring ac4C modification in laryngeal squamous cell ...
Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the development of precision immunotherapies and vacc...
Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic interplay between tumors, microbes, and t...
The rapid progress of multimodal large language models (MLLMs) has led to increasing interest in agent-based systems. While most prior work in medical...