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CoralBay: A Self-Supervised CT Foundation Model
arXiv:2606.03888v1 Announce Type: new Abstract: Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effectively across tasks. However, many medical imaging modalities, such as CT scans, are inherently three-dimensional and differ fundamentally from natural images in both structure and semantics. Volumetric modalities capture spatial continuity, organ anatomy, and intensity-based tissue properties...
RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods largely relegate clinicians to passive observers of final outputs, offering no interpretable reasoning trace for them to inspect, validate, or refine. To address this, we introduce RadAgent, a tool-using AI agent that generates CT reports through a stepwise and interpretable process.
Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation
Announce Type: new Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and output diversity, collapsing to generic templates that under-report rare yet critical findings. We identify this failure mode as Template Collapse. This failure stems from the unique constraints of 3D medical imaging, e.g., limited data, severe label imbalance, and weak signals from volumetric encoders.
Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
arXiv:2606.04365v1 Announce Type: new Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion...
The Cross-Architecture Substrate: A Domain-Transcendent, Calibration-Surviving Geometric Invariant of Modern Vision Encoders
arXiv:2606.07882v1 Announce Type: new Abstract: Different vision neural networks -- trained to classify, contrast, reconstruct, or match images to text -- should have correspondingly different internal representations. We report that they do not.
Dance teacher avoids jail after 'prank gone wrong' leaves friend with brain damage
Dance teacher avoids jail after 'prank gone wrong' leaves friend with brain damage The victim was sat with another friend in the boot of Evie Robinson’s Audi A1 when they suddenly fell out as the then 19-year-old drove across a car park in Mansfield A dance teacher has narrowly avoided jail after her 18-year-old friend suffered horrific brain injuries falling out of the boot of her car in a "prank" which went wrong. The victim was sat with another friend in the boot of Evie Robinson’s Audi...
Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel
arXiv:2606.02156v1 Announce Type: cross Abstract: Anastomotic leak remains one of the most serious complications following colorectal cancer surgery, substantially affecting patient outcomes, recovery trajectories, and healthcare costs. Despite advances in imaging technology, current preoperative assessment relies only on clinical assessment, a process that is subjective, error-prone, and highly dependent on individual expertise.
MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts
Announce Type: replace Abstract: Generative models for volumetric medical images have found many applications in medical imaging, ranging from data augmentation to serving as priors for inverse problems. For these applications, generating high-resolution 3D images with strong controllability is essential but remains highly challenging. Existing approaches typically control generation either through radiology reports used as text prompts or through full image segmentation.
When is 3D Worth It? A Resource-Performance Frontier for CNNs and Transformers in Lung CT
arXiv:2606.06950v1 Announce Type: new Abstract: Three-dimensional models are widely assumed preferable for volumetric medical imaging, yet their practical value depends on whether performance gains justify added computational cost and complexity. Rather than proposing a new architecture, we study how input dimensionality (2D, 2.5D, 3D) affects model behavior across convolutional neural networks (CNNs) and Vision Transformers (ViTs) under a fixed training protocol.
Mum-of-five in coma after contracting sepsis in Gran Canaria as family desperate to get her 'home with babies'
Mum-of-five in coma after contracting sepsis in Gran Canaria as family desperate to get her 'home with babies' Emily Casey, 34, from Wirral, was put into an induced coma after falling ill with pneumonia and sepsis on holiday in Gran Canaria with her husband Jamie and their five children, aged one to 13 — her family are desperately raising £50,000 for a medical flight home The family of a mum-of-five who is stuck in hospital in Gran Canaria after contracting sepsis have said they "just want...