arXiv:2609.16032v1 Announce Type: cross Abstract: Diffusion-based artifact detectors score whole-slide image patches by reconstruction error under a model fine-tuned on clean tissue. We show that con...
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.
The paper introduces two lightweight spectral adapters—Directional Spectral Adapter (DiSECT) and Spectral Instance-Guided Adapter (SiGA)—to adapt the Segment Anything Model (SAM) for accurate segmentation of colorectal liver metastases in contrast‑enhanced CT scans. SiGA achieves the highest single‑point Dice score of 0.77 and performs comparably to a 3D nnU‑Net baseline under no‑prompt inference, while DiSECT requires only 0.14 million trainable parameters. The study evaluates the adapters on 446 CT volumes across various prompting regimes, demonstrating that spectral adapters can efficiently adapt SAM with limited trainable parameters while maintaining strong segmentation accuracy.
The study introduces a voxel-spacing-aware radiomic framework that incorporates physical geometry into texture computation without resampling, modifying PyRadiomics to preserve native image signals. Four extraction configurations—native non-resampled (NR), isotropic resampling (RS), voxel-spacing-aware (VS), and fake-isotropic preprocessing (FK)—were compared across 685 CT pulmonary nodules and 209 MRI breast cases, evaluating 196 radiomic descriptors. Results show that VS closely matches NR (median ICC(A,1) ≈0.998) while RS and FK exhibit lower agreement, indicating that spacing metadata alone can significantly influence radiomic features.
arXiv:2608. 08046v1 Announce Type: new Abstract: Text-conditioned chest X-ray generation aims to synthesize realistic radiographs that faithfully depict specified findings.
The paper introduces a one‑pass conditional 3D rectified flow (3D Flow) framework for denoising whole‑body PET images, employing an optimized non‑uniform sampling strategy and a linear‑interpolant velocity‑matching objective. It reconstructs a full 3D volume in about 30 seconds, dramatically faster than multi‑hour 3D diffusion models, while maintaining high global image quality and lesion conspicuity even at ultra‑low doses (down to 1/100 of standard). Zero‑shot transfer tests on independent clinical data demonstrate robust performance across datasets and unseen dose levels.
arXiv:2608.28773v1 Announce Type: cross Abstract: Perceptual losses are widely used in medical image synthesis because they encourage agreement in high-level structure beyond voxel-wise intensity sim...
arXiv:2508.17299v2 Announce Type: replace Abstract: Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despi...
This study introduces a two‑stage vision‑language model framework to assess the clinical quality and usability of late gadolinium enhancement (LGE) cardiac MRI images used for atrial fibrillation ablation planning. The first stage employs a fine‑tuned VLM to generate structured radiology‑style reports on five quality criteria—Noise, Motion Artifact, LA Boundary Accuracy, PV Region Accuracy, and Under‑segmentation Severity—while the second stage uses a GPT‑based reasoning module to convert these reports into structured quality scores and a binary decision on clinical usability. Evaluated on a curated dataset of 60 image‑slice and text‑pair annotations from 20 patients, the InternVL2 model achieved the highest criterion‑level accuracy, and DeepSeek reached perfect agreement on the clinical usability decision.
arXiv:2608.28716v1 Announce Type: cross Abstract: Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent impr...
arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...
The study evaluates a deep‑learning model for pediatric pneumonia detection across chest X‑ray datasets from three countries, assessing discrimination, calibration, operating‑point transport, shortcut signals, and limited‑label recovery. Using a frozen DenseNet121 ensemble trained on Guangzhou data, the model achieved high internal AUROC (0.976) but performance dropped when applied zero‑shot to Bangladesh (AUROC 0.798) and Vietnam (AUROC 0.742). Limited‑label adaptation with Platt recalibration restored sensitivity but introduced significant specificity variability, highlighting the need to evaluate multiple performance dimensions in cross‑dataset transport studies.