arXiv:2608.30835v1 Announce Type: cross Abstract: Background and Objective: Quality control is a prerequisite for whole-slide image analysis, yet the benchmarks on which quality-control methods are c...
arXiv:2609.40083v1 Announce Type: cross Abstract: One-class artifact detectors for whole-slide images learn normal tissue from a clean training pool and flag departures from it. The pool is built by...
One-class artifact detectors for whole-slide images learn normal tissue from a clean training pool and flag departures from it. The pool is built by a preprocessing pipeline whose tissue-detection ste...
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.
The Natural Image Quality Evaluator (NIQE) scores an image by its statistical distance from a model fitted on pristine images, and its distributed model is fitted on photographs. We release the Radiol...
arXiv:2609.24116v1 Announce Type: new Abstract: Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing...
CATCH is a conditional 3D diffusion model operating in an invertible Haar-wavelet domain designed to inpaint masked regions in T1‑weighted brain MRI with plausible, tumor‑free tissue while preserving observed anatomy. The model’s denoiser uses noisy target coefficients, voided‑image coefficients, and a signed mask, guided by tumor‑excluded wavelet reconstruction and a hole‑focused loss, and hard compositing ensures observed voxels remain unchanged. Experiments on BraTS data show that a weighted mixture of tumor‑derived, irregular‑blob, and ellipsoidal masks yields the best performance, achieving higher SSIM, PSNR, and lower MSE compared to fixed or random augmentation baselines.
arXiv:2609.31670v2 Announce Type: replace Abstract: We study rectified flows for denoising without clean training targets. For an unconditional rectified flow trained on Noise2Noise pairs, the optima...
The paper introduces a counterfactual data augmentation method that generates healthy mammograms by inpainting lesions with realistic normal tissue using a Denoising Diffusion Probabilistic Model and a RePaint-based sampling strategy. The generated healthy counterfactuals preserve patient-specific anatomy and match the quality of the original dataset, as confirmed by similarity metrics and radiologist assessment. When applied to four different classifier architectures on the VinDr-Mammo dataset, the augmentation improves sensitivity, especially at 80% fixed specificity, thereby enhancing the reliability of computer-aided breast cancer screening systems.
The paper introduces SlideRuler, a method that uses internal controls within a pathology slide to estimate and correct scanner‑induced shifts in tissue representation. By learning a transfer map from paired rescans, SlideRuler can calibrate a frozen foundation model from a single scan, reducing embedding distance by 16.3‑38.5% across two encoders and five SCORPION scanners. A source‑anchored variant further reduces source‑feature displacement while preserving most alignment gains, demonstrating consistent calibration across imaging systems.
arXiv:2607. 05965v1 Announce Type: cross Abstract: Vascular computed tomography datasets are commonly annotated only once per scan, yielding the pervasive yet under addressed problem of single mask annotation noise.
The paper introduces SWIFT, a Swin V2‑based model pretrained on 10,444 3D CT volumes and fine‑tuned for rectal cancer segmentation on T2‑weighted MRI. Four configurations—full fine‑tuning (SWIFT), decoder compression (SWIFTe), low‑rank adaptation (SWIFTe‑LoRA), and a LoRA‑decoder ensemble (SWIFTe‑LDE4)—were evaluated on 247 cases, showing that SWIFTe reduces parameters by 70.1% while improving tumor detection and radiomic agreement. The study also demonstrates a trade‑off between detection and boundary agreement, and highlights that SWIFTe‑LDE4 achieves the lowest calibration errors after temperature scaling.