The study introduces MitPro, an AI tool that guides pathologists to regions with high mitotic activity and highlights mitotic figures, aiming to reduce variability and time in tumour grading. In a retrospective paired reader study of 385 whole‑slide images across seven tumour types, AI assistance raised the intraclass correlation coefficient from 0.589 to 0.949 and cut median assessment time from 286.4 to 127.8 seconds. The tool also produced a slight increase in mitotic counts, aligning with the detection of more active hotspots, while the frequency of score changes matched typical inter‑pathologist variation.
arXiv:2606. 07368v1 Announce Type: cross Abstract: Automated mitosis detection is a well-established task in computational pathology.
arXiv:2608. 12590v1 Announce Type: new Abstract: Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these tasks in isolation and provide limited support for clinical review.
Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type and image acquisition. We present MiTHras, a task-spe...
arXiv:2609.16597v1 Announce Type: cross Abstract: Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapp...
Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapping imaging features across tumor types, inter-obse...
arXiv:2608. 03079v1 Announce Type: cross Abstract: Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap can obscure subtype distinctions.
arXiv:2609.24736v1 Announce Type: cross Abstract: Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type an...
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...
The study investigates how confidence intervals (CIs) behave in medical imaging AI by analyzing 24 segmentation and classification tasks with 19 models per task, various metrics, aggregation strategies, and CI methods. It finds that required sample sizes for reliable CIs vary widely, CI behavior depends on performance metrics, aggregation strategy, and problem type, and that different CI methods differ in reliability and precision. The authors provide a decision tree to guide researchers in selecting appropriate CI methods, aiming to support future consensus guidelines on reporting performance uncertainty.
arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
RACR-MIL is a weakly‑supervised method for grading squamous cell carcinoma (SCC) from whole‑slide images, using an attention‑based multiple‑instance learning framework. It introduces a hybrid WSI graph to capture local tissue context and non‑local phenotypic dependencies, and applies rank‑ordering constraints on attention to prioritize higher‑grade tumor regions, mirroring pathologists’ diagnostic reasoning. The approach achieves state‑of‑the‑art performance, improving SCC grading accuracy by 3–9% over existing methods and up to 10% in tumor localization, and a pilot study showed pathologists reported increased grading efficiency in 60% of cases.
Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological asses...