Latest AI and machine learning research in oncology/hematology for healthcare professionals.
This study employs a robust analytical framework to uncover patterns in survival outcomes among breast cancer patients from diverse racial and geographical backgrounds. This research uses the SEER 2021 dataset to analyze breast cancer survival outcomes to identify and comprehend dissimilarities. Our approach integrates exploratory data analysis (EDA), through this we identify key variables that ...
Cancer subtype classification is crucial for personalized treatment and prognostic assessment. However, effectively integrating multi-omic data remains challenging due to the heterogeneous nature of genomic, epigenomic, and transcriptomic features. In this work, we propose Modality-Aware Cross-Attention MoXGATE, a novel deep-learning framework that leverages cross-attention and learnable modalit...
Biomolecular networks, such as protein-protein interactions, gene-gene associations, and cell-cell interactions, offer valuable insights into the co...
Pathology foundation models (PFMs) have emerged as powerful tools for analyzing whole slide images (WSIs). However, adapting these pretrained PFMs f...
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Contrastive self-su...
Among the genetic algorithms generally used for optimization problems in the recent decades, quantum-inspired variants are known for fast and high-f...
Time-resolved CT is an advanced measurement technique that has been widely used to observe dynamic objects, including periodically varying structure...
In nontargeted spatial metabolomics, accurate annotation is crucial for understanding metabolites' biological roles and spatial patterns. MS mass spec...
Skin cancer is among the most prevalent and life-threatening diseases worldwide, with early detection being critical to patient outcomes. This work ...
Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive a...
The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with hi...
Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in ...
Learning on small data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble m...
Deformable medical image registration is an essential task in computer-assisted interventions. This problem is particularly relevant to oncological ...
Gallbladder cancer has an insidious onset,and most of the cases are in advanced stage at the time of diagnosis,with unfavorable prognosis. Radical sur...
The intracellular pH (pH) is critical for understanding various pathologies, including brain tumors. While conventional pH measurement through P-MRS s...
Accurate and timely diagnosis of brain tumors is critical for patient management and treatment planning. Magnetic resonance imaging (MRI) is a widely ...
Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to i...
Radiologists are witnessing astonishing innovation and advancement of CT technologies and their clinical applications. This review highlights how phot...
Skin cancer encompasses a diverse spectrum of malignancies with increasing global incidence and persistent clinical challenges. Despite advances in th...