arXiv subjects

Xiwen Wu

Publications and source records attributed to Xiwen Wu.

4 recordsLinked to original sources

arXiv2026

LiverPlan: A Stage-Adaptive Immersive Visual Analytics Framework for Anatomical Liver Surgical Planning

Anatomical liver resection (ALR) surgery is the most important treatment for liver cancer, yet preoperative planning demands complex, multi-stage clinical reasoning under competing safety constraints. Current 2D desktop tools are not well equipped to support this process, exhibiting three fundamental limitations: reliance on monolithic interfaces that fail to adapt to the distinct cognitive demands of each planning stage; a perceptual bottleneck caused by limited anatomical spatial representation and missing plane-vessel intersection visualization; and an attention bottleneck stemming from fragmented critical safety criteria display across separate views. We present LiverPlan, a stage-adaptive immersive visual analytics framework for ALR planning, grounded in an 8-month collaboration with two expert hepatobiliary surgeons. Decomposing the surgical planning process into three sequential yet cognitively distinct stages, LiverPlan externalizes the cognitive demand of each stage via tailored techniques: (1) context-preserving focus and hue-preserving rendering for anatomical discovery; (2) direct 3D resection plane manipulation coupled with real-time, embedded visual feedback on critical safety criteria during plan refinement; and (3) explicit plane-vessel intersection visualization for anticipatory surgery preparation. A within-subjects study with eight hepatobiliary surgeons against a desktop baseline shows large-effect-size improvements in task completion time, perceived cognitive workload, and system usability on controlled planning tasks. Moreover, our study reveals broader insights: LiverPlan reduces cognitive burden and encourages a shift in surgeons from merely satisfying safety criteria to actively optimizing them, suggesting that explicit visualization of spatial relationships lowers the cognitive barrier to complex surgical planning.

cs.HC↗

arXiv2026

MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models

The increasing use of vision-language models (VLMs) in healthcare applications presents great challenges related to hallucinations, in which the models may generate seemingly plausible results that are in fact incorrect. Such hallucinations can jeopardize clinical decision making, potentially harming the diagnosis and treatments. In this work, we propose MedHallTune, a large-scale benchmark designed specifically to evaluate and mitigate hallucinations in medical VLMs. Comprising over 100,000 images and 1,000,000 instruction pairs, MedHallTune includes both hallucination and non-hallucination samples, each with ground-truth annotations. We conduct a comprehensive evaluation of current medical and general VLMs using MedHallTune, assessing their performance across key metrics, including clinical accuracy, relevance, detail level, and risk level. The experimental results show that fine-tuning with MedHallTune successfully improves the ability of several existing models to manage hallucinations and boost their zero-shot performance on downstream visual-question-answering (VQA) tasks, making them more reliable for practical medical applications. Our work contributes to the development of more trustworthy VLMs. Codes and dataset will be available at \href{https://github.com/russellyq/MedHallTune}{MedHallTune}.

cs.AI↗

arXiv2026

Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k

Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quantity, and greater demand for training compute. In this report, we present Open-Sora 2.0, a commercial-level video generation model trained for only $200k. With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable. We detail all techniques that contribute to this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. According to human evaluation results and VBench scores, Open-Sora 2.0 is comparable to global leading video generation models including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, we aim to democratize access to advanced video generation technology, fostering broader innovation and creativity in content creation. All resources are publicly available at: https://github.com/hpcaitech/Open-Sora.

cs.GR↗

arXiv2025

Coordinated 2D-3D Visualization of Volumetric Medical Data in XR with Multimodal Interactions

Volumetric medical imaging technologies produce detailed 3D representations of anatomical structures. However, effective medical data visualization and exploration pose significant challenges, especially for individuals with limited medical expertise. We introduce a novel XR-based system with two key innovations: (1) a coordinated visualization module integrating Multi-layered Multi-planar Reconstruction with 3D mesh models and (2) a multimodal interaction framework combining hand gestures with LLM-enabled voice commands. We conduct preliminary evaluations, including a 15-participant user study and expert interviews, to demonstrate the system's abilities to enhance spatial understanding and reduce cognitive load. Experimental results show notable improvements in task completion times, usability metrics, and interaction effectiveness enhanced by LLM-driven voice control. While identifying areas for future refinement, our findings highlight the potential of this immersive visualization system to advance medical training and clinical practice. Our demo application and supplemental materials are available for download at: https://osf.io/bpjq5/.

cs.HC↗