Hugging Face Trending Papers
RIQE: a NIQE-style reference model for Computed Tomography
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Computer Vision
Oct 2
RIQE: a NIQE-style reference model for Computed Tomography
The paper introduces RIQE, a reference-free image quality evaluator for computed tomography that mirrors the NIQE framework used for photographs. Trained on 3,792 full‑dose CT slices from 158 patients, RIQE consistently ranks reduced‑dose reconstructions lower than their full‑dose counterparts and correctly identifies increased noise levels, but its preferences for filtered images do not align with lesion visibility. The authors caution that RIQE should not be the sole metric for selecting or tuning denoising algorithms.
arXiv Computer Vision
Sep 16
Conditioning noise is a free regularizer for LoRA fine-tuning: no pathology encoder required for diffusion-based artifact detection in histopathology
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 AI
Jul 24
Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.
arXiv AI
Sep 15
Physically Aware Radiomics Without Interpolation: Disentangling Voxel Geometry and Signal Modification in CT and MRI
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 AI
Aug 11
JustLLMGRPO: Radiographic Control for Chest X-Ray Generation
arXiv:2608. 08046v1 Announce Type: new Abstract: Text-conditioned chest X-ray generation aims to synthesize realistic radiographs that faithfully depict specified findings.
arXiv AI
Oct 16
Towards Better Aligned LLM Judges for Radiology Report Evaluation across Modalities and Anatomies
The paper evaluates large language model (LLM) judges for radiology report assessment across multiple imaging modalities and anatomical regions, comparing four existing metrics (RadFact, GREEN, FineRadScore, CRIMSON) with a new metric called VERT. GREEN emerges as the most robust among current metrics, while VERT improves correlation with radiologist judgments by up to 26% over GREEN. The study also shows that ensembling and lightweight fine‑tuning of the Qwen3‑30B model can yield additional performance gains and significant latency reductions.