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...
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: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...
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.
MIRTO is an evaluation protocol for unsupervised anomaly segmentation in brain MRI that explicitly documents key methodological choices—such as registration alignment, threshold setting, and false‑positive budgeting—and measures their impact. It applies a registration check, uses validation data for thresholding, reports realized false‑positive volumes, and repeats each comparison across 15,552 evaluation pipelines with bootstrap intervals. In a study on four UAD methods and 312 BraTS 2020 subjects, MIRTO revealed that an axis‑order mismatch dramatically lowered a diffusion model’s voxel AUROC, and that many performance differences were driven by lesion definition and threshold transfer rather than model quality.
arXiv:2608. 03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications.
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.
arXiv:2609.37848v1 Announce Type: cross Abstract: Biomedical machine learning papers often compress model performance into one headline number. That number can look like a property of the model even...
The paper introduces TRUST, a threshold‑recalibrated training method that dynamically adjusts the dismissal threshold during training to penalize cancer‑positive images near the dismissal region. Evaluated on NLBS and RSNA datasets, TRUST achieved higher case‑level dismissal rates while maintaining 98% and 95% recall, outperforming a cross‑entropy baseline. External validation on RSNA→NLBS data confirmed improved dismissal rates at both recall targets, demonstrating the effectiveness of closed‑loop threshold‑aware training for selective dismissal in breast cancer screening.
arXiv:2609.00704v1 Announce Type: new Abstract: Functional tissue units (FTUs), including tertiary lymphoid structures (TLSs), blood vessels, and glands, encode localized immune, vascular, and epithe...
arXiv:2608. 14633v1 Announce Type: cross Abstract: Wolff-Parkinson-White (WPW) syndrome is a congenital cardiac pre-excitation, clinically important and often missed on the resting 12-lead ECG.
Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level specificity. Materials and Methods: This retrospective study used PI-CAI (5-fold cross-validation) and Prostate158 (n=158; external).