arXiv:2606. 02156v1 Announce Type: cross Abstract: Anastomotic leak remains one of the most serious complications following colorectal cancer surgery, substantially affecting patient outcomes, recovery trajectories, and healthcare costs.
arXiv:2607. 29090v1 Announce Type: new Abstract: Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identification of high-risk patients and targeted perioperative care.
arXiv:2606. 30657v1 Announce Type: cross Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself.
arXiv:2608. 20154v1 Announce Type: new Abstract: Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes.
This survey reviews recent advances in surgical video generation, categorizing methods into unconditional, conditional, and world modeling generation. It highlights a shift from creating visually plausible frames to modeling the causal dynamics of surgical scenes, and discusses challenges such as pixel-level fidelity versus clinical plausibility, generalization, physical realism, controllability, and interpretability. The paper also compiles experimental results from public datasets to serve as a quantitative benchmark for the field.
arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.
arXiv:2411. 05824v4 Announce Type: replace-cross Abstract: Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment.
MedVA is an end‑to‑end neuro‑symbolic agentic system designed to streamline medical volume visualization. It combines a neuro‑symbolic intent formulation agent that refines natural‑language requests with symbolic reasoning, a multi‑model ROI identification agent that uses pretrained medical segmentation models to locate specified regions, and an objective‑driven visualization optimization agent that evaluates ROI visibility using a volume‑based objective. Extensive evaluations and a formative user study demonstrate the system’s effectiveness and high usability across users with varying expertise.
NV-Reason-CT is a generative vision‑language model designed for chest and abdominal CT analysis that preserves native 3D visual encoding and incorporates radiologist‑guided reasoning. The system couples a 3D vision transformer with a language model, feeding all visual tokens and their 3D coordinates directly into language decoding to maintain volumetric spatial information. Trained on a curated corpus of about 550,000 multimodal instruction examples, the model supports abnormality classification, report generation, and interactive reasoning, achieving strong performance on CT benchmarks and reducing expert interpretation time by 50%.
arXiv:2409.16183v2 Announce Type: replace Abstract: Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in m...
arXiv:2610.00414v1 Announce Type: new Abstract: Foundation models pretrained on large-scale datasets demonstrate strong transferability to medical imaging tasks. However, understanding how their late...
Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence.
OncoVision is a privileged‑information training framework that learns from mammography images and clinical data during training but performs inference using only mammographic images. It employs an attention‑based encoder‑decoder to jointly segment masses, calcifications, axillary findings, and breast tissue, and predicts ten structured clinical features such as BI‑RADS. Two late‑fusion strategies (Independent and Dependent) integrate imaging, radiomic, and clinical information to improve diagnostic precision, and a retrospective multi‑reader study showed higher diagnostic confidence, reduced reading time, and segmentation accuracy comparable to or better than radiologists.