Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress at the patch and whole-slide image levels, representation learning at the single-cell level remains comparatively underexplored, despite its importance for characterizing cell types and cellular phenotypes. We introduce L...
Tumor phylogenies - rooted trees encoding clonal ancestry and mutation acquisition - are central to understanding cancer evolution, yet generating realistic phylogenies remains challenging. We investigate whether discrete graph diffusion can learn the structural constraints of tumor phylogenies directly from data. Working with approximately 12,500 synthetic phylogenies across twelve evolutionary r...
In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detec...
Background: Manual chart abstraction is a major bottleneck in clinical research. In oncology, important outcomes such as disease recurrence and the tr...
Deep learning and generative models are advancing rapidly, with synthetic data increasingly being integrated into training pipelines for downstream an...
Background: Gene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution ap...
Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and m...
Background: Imaging-based breast cancer risk prediction models primarily use full-field digital mammography (FFDM). As digital breast tomosynthesis (D...
Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imaging (...
Computer-aided segmentation of brain tumors from MRI data is of crucial significance to clinical decision-making in diagnosis, treatment planning, and...
Accurate prediction of synthetic lethality (SL) is important for guiding the development of cancer drugs and therapies. SL prediction faces significan...
Cancer data standardization requires converting unstructured pathology reports into structured registry variables, a mostly manual and resource-intens...
The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires ...
Whole Slide Images (WSIs) are giga-pixel in scale and are typically partitioned into small instances in WSI classification pipelines for computational...
Brain tumor MRI segmentation is essential for clinical diagnosis and treatment planning, enabling accurate lesion detection and radiotherapy target de...
Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors, of which 15-25% develop metastatic disease with 5-year survival rates rep...
Brain tumor segmentation remains challenging because the three standard sub-regions, i.e., whole tumor (WT), tumor core (TC), and enhancing tumor (ET)...
Accurate classification of brain tumors from magnetic resonance imaging (MRI) plays a critical role in early diagnosis and effective treatment plannin...
We present the first study of Hyper-Connections (HC) for volumetric multi-modal brain tumor segmentation, integrating them as a drop-in replacement fo...
Background Scalable, non invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We e...