Whole-slide image (WSI) analysis remains computationally challenging due to the extremely large spatial resolution of slides and the sparse distribution of tumour regions. We propose an end-to-end reinforcement learning framework for sequential tumour segmentation directly on WSIs.
arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
arXiv:2507. 05077v5 Announce Type: replace-cross Abstract: Deep neural networks are increasingly applied in automated histopathology.
The paper introduces a pipeline that uses publicly available whole slide image foundation models (FMs) to automatically triage slides by ranking them based on zero‑shot classification predictions. This approach accurately identifies slides containing the most tumor, achieving top‑2 ranking for patients with up to 43 slides across multiple datasets. The study also proposes a ranked evaluation framework to benchmark FM performance in slide triage.
arXiv:2505.03380v2 Announce Type: replace Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
arXiv:2608.30420v1 Announce Type: cross Abstract: Automating the analysis of whole-slide images has high clinical value, since characterizing cancers requires examining them in detail. Such analysis...
The paper introduces RA-CMF, a Region‑Adaptive Conditional MeanFlow framework for CT image reconstruction. It combines a conditional MeanFlow network that predicts flow fields for image refinement with a reinforcement‑learning driven policy that allocates tile‑wise refinement budgets. The method achieves high reconstruction quality, reporting a tumor ROI radiomic feature CCC of 0.93 ± 0.09, PSNR of 31.94 ± 2.64, SSIM of 0.97 ± 0.03, and overall PSNR of 34.23 ± 1.71 and SSIM of 0.95 ± 0.01.
The paper introduces the Consistency Memory Bank (COMB), a label‑free virtual staining framework designed to process gigapixel Whole Slide Images without the memory bottlenecks of patch‑based deep learning. COMB decouples context storage from computation, using a dynamic retrieval mechanism to fetch feature representations from adjacent tiles, local padding to resolve spatial discontinuities, and neighbor‑aware channel attention to stabilize statistical drift. The method achieves superior perceptual fidelity and tiling consistency compared to state‑of‑the‑art baselines, and its improved continuity suggests downstream benefits for tumor segmentation.
The paper introduces Report Supervision (R‑Super), a framework that uses radiology reports to supervise tumor segmentation models. By incorporating loss functions that align segmentation outputs with report‑derived tumor counts, sizes, and locations, R‑Super improves detection and segmentation performance. Experiments on kidney and pancreatic tumors show up to a 15% increase in F1‑Score and DSC compared to mask‑only training, outperforming methods like CLIP and multi‑task learning.
LanGuSTE is a patch‑selection framework for whole slide image analysis that uses vision‑language models and large language model knowledge. It introduces Cross‑Scale Visual Prompt Tuning to align low‑resolution and high‑resolution patches, and a coarse‑to‑fine selection module that encodes only informative high‑resolution patches. Experiments show LanGuSTE cuts overall processing time to about one‑third of the baseline while matching or surpassing diagnostic performance of exhaustive and state‑of‑the‑art methods.
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...
arXiv:2603. 26720v3 Announce Type: replace-cross Abstract: Predicting surgical needle trajectories from endoscopic video is critical for robot-assisted suturing, enabling anticipatory planning, real-time guidance, and safer motion execution.
PCaPaint is a prostate cancer inpainting method that uses latent diffusion models (LDMs) and introduces a conditioning strategy where the condition image is filled with Gaussian noise to mitigate shortcut learning. It also proposes a new training objective that focuses on errors within the lesion region and a multi‑sequence latent design that separately compresses T2w and DWI&ADC scans to preserve their distinct frequency characteristics. Experiments show that the synthetic data generated by PCaPaint improves downstream tasks such as prostate lesion segmentation, patient‑level classification, and lesion‑level detection, outperforming recent state‑of‑the‑art LDM‑based tumor inpainting methods in both performance and image quality.