Latest AI and machine learning research in lymphoma for healthcare professionals.
When sighted practitioners author accessible data visualizations, they build navigation structures (the nodes, edges, and input bindings that govern how assistive technologies traverse an interface) entirely in code, with no visual representation. Without a representation to react to, practitioners cannot develop judgment about what makes navigation good or bad, and the quality ceiling of non-visu...
Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions. Although recent foundation models such as the Segment Anything Model (SAM) provide strong general-purpose segmentation priors, their natural-image pre-training transfers poorly to the AM XCT domain, where d...
Computed Tomography (CT) diagnosis often relies on dynamic selection of imaging phases, such as non-contrast, arterial, or venous phases, based on pre...
Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compre...
Precise, non-invasive manipulation of individual living cells remains a central challenge in biomedical science, with far-reaching implications for si...
Purpose: To implement a comprehensive knowledge-based algorithm (KBA) for pancreatic cancer staging based on the current Japanese guidelines and to ev...
Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such a...
Motivation: Cell-type annotation in spatial transcriptomics is challenging due to sparse gene panels, spatial heterogeneity, and limited availability ...
BackgroundFor patients with metastatic gastrointestinal cancers, chemotherapy resistance is a common phenomenon that, if known in advance, would allow...
Purpose: To evaluate the feasibility and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translat...
Purpose: To develop and validate a temporal deep learning framework for predicting geographic atrophy (GA) progression across multi-year horizons usin...
Accurate immune cell classification is essential for interpreting single-cell RNA sequencing (scRNA-seq) data. However, progress in automating cell ty...
Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measure...
Abstract Purpose Radiologic surveillance is essential for oropharyngeal cancer (OPC) survivors, guiding recurrence detection and follow-up strategies....
Sampling from high-dimensional, non-log-concave distributions with unnormalized densities is a fundamental challenge in machine learning, particularly...
There is interest in the use of recent single-cell spatial transcriptomic technologies to gain biological insights into disease mechanisms. Previously...
Multimodal attributed graphs (MAGs) integrate graph topology with heterogeneous modality attributes, such as text and images, thereby enabling richer ...
Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical s...
Accurate prediction of overall survival (OS) from positron emission tomography/computed tomography (PET/CT) can support personalized treatment and fol...
In this paper, we present a novel set of related models for semantic segmentation of node-link diagrams. These diagrams are frequently used to represe...