Lung CT
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Related Articles from SNS
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.
Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models
arXiv:2605.30631v1 Announce Type: new Abstract: While automated diagnosis systems have achieved remarkable success in computed tomography (CT)-based lung cancer screening, their development remains limited by the scarcity of diverse, annotated pulmonary nodule datasets. Diffusion-based generative models offer a promising strategy for data synthesis; however, many existing conditional approaches primarily optimize spatial reconstruction losses, which encourage voxel-wise similarity but may...
Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging
arXiv:2606.02809v1 Announce Type: new Abstract: Evaluating vision-language models (VLMs) on medical images requires benchmarks that are clinically grounded, scalable, and controlled for evaluation confounds. Existing public benchmarks are limited in scale, manually annotated, or potentially leaked into VLM pretraining corpora. We present an automated agent-driven pipeline that generates multiple-choice VQA datasets directly from paired private radiology reports and 3D oncology imaging,...
Foveated-Imaging Geometry CT Architecture and Seeded Diffusion Model Enabling Global Super-Resolution Reconstruction
Announce Type: new Abstract: For X-ray computed tomography (CT), a smaller detector pixel size generally leads to higher scanner spatial resolution, but inevitably increases system cost as well as data overhead in acquisition and processing. To achieve high-resolution (HR) CT imaging in a more resource-efficient manner, we propose a Foveated-Imaging Geometry CT (FIGCT) architecture, which integrates local HR data into an acquisition scheme dominated by low-resolution (LR) measurements. We...
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...
J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization
Announce Type: replace Abstract: Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, whereas AI-based models automate this process but often underperform with limited data and domain shifts. Inspired by how pathology trainees acquire disease recognition skills through guided comparison with expert-annotated slides and histopathology atlas reference images, we propose Joint Retrieval-Augmented Segmentation (J-RAS). This framework...
Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection
arXiv:2606.04453v1 Announce Type: new Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for building reliable predictive models. This study proposes a Gradient-Loss Recursive Feature Elimination (GL-RFE) framework that integrates gradient sensitivity analysis...
Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation
Announce Type: new Abstract: Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, training CT-specific VAEs from scratch or heavily fine-tuning them incurs substantial computational and engineering cost, and often degrades under heterogeneous scanners, protocols, and diseases. This paper makes a progressive stride toward training-free medical VAEs by leveraging a critical observation: a single...
The forgotten organ that could predict how long you live
The forgotten organ that could predict how long you live A forgotten immune organ may be one of the strongest hidden predictors of longevity, disease risk, and cancer treatment success. - Date: - June 1, 2026 - Source: - Mass General Brigham - Summary: - A long-overlooked organ may hold surprising clues to healthy aging and cancer survival.