Accurate visualization of subtle vascular dynamics is a knowledge-intensive challenge in minimally invasive surgery. Conventional imaging systems struggle to reveal these imperceptible motions amidst the dynamic complexity of surgical scenes, limiting decision-making reliability. We introduce EndoControlMag , a Lagrangian framework that employs mask-conditioned magnification to selectively enhance vascular motion while preserving the structural integrity of surrounding tissues in endoscopic videos. Our approach integrates two key designs: Periodic Reference Resetting (PRR) , which divides videos into short overlapping clips with dynamically updated reference frames to alleviate error accumulation while maintaining temporal coherence, and Hierarchical Tissue-aware Magnification (HTM) , which combines pretrained visual tracking for accurate vessel localization with dual-mode adaptive softening strategies. HTM employs either motion-based softening that modulates magnification strength proportional to observed tissue displacement, or distance-based exponential decay that simulates biomechanical force attenuation. This strategy enables robust performance across diverse surgical scenarios where motion-based softening excels with complex tissue deformations and distance-based softening provides stability under unreliable optical flow conditions. To validate generality and scalability, we construct EndoVMM24, a benchmark dataset spanning four surgical specialties and diverse intraoperative scenarios. Extensive quantitative metrics, qualitative assessments, and expert surgeon evaluations demonstrate that EndoControlMag significantly outperforms existing methods in magnification accuracy, image quality, and robustness. This work advances engineering informatics for surgical vision by providing a reproducible, context-aware framework that supports reliable decision-making in minimally invasive procedures. The code, dataset, and video results are available at https://cho-haz.github.io/EndoControlMag/ .

Clinical diagnosis education requires students to master both systematic reasoning processes and comprehensive medical knowledge.While recent advances in Large Language Models (LLMs) have enabled various medical educational applications, these systems often provide direct answers that could reduce students' cognitive engagement and lead to fragmented learning.Motivated by these challenges, we propose DDxTutor, a framework that follows differential diagnosis principles to decompose clinical reasoning into teachable components.It consists of a structured reasoning module that analyzes clinical clues and synthesizes diagnostic conclusions, and an interactive dialogue framework that guides students through this process.To enable such tutoring, we construct DDxReasoning, a dataset of 933 clinical cases with fine-grained diagnostic steps verified by doctors.Our experiments demonstrate that fine-tuned LLMs achieve strong performance in generating structured teaching references and conducting interactive diagnostic tutoring dialogues.Human evaluation by medical educators and students validates the framework's potential and effectiveness for clinical diagnosis education.Our project is available at https://github.com/med-air/DDxTutor.

The evolution of text-to-image (T2I) generation techniques has introduced new capabilities for information visualization, with the potential to advance knowledge democratization and education. In this paper, we investigate how T2I models can be adapted to generate educational health knowledge contents, exploring their potential to make healthcare information more visually accessible and engaging. We explore methods to harness recent T2I models for generating health knowledge flashcards—visual educational aids that present healthcare information through appealing and concise imagery. To support this goal, we curated a diverse, high-quality healthcare knowledge flashcard dataset containing 2,034 samples sourced from credible medical resources. We further validate the effectiveness of fine-tuning open-source models with our dataset, demonstrating their promise as specialized health flashcard generators. Our code and dataset are available at: https://github.com/med-air/HealthCards.

Deep neural networks (DNNs) have proved their great potential over various perceptual and cognitive tasks with the cost of ever-growing storage capacity and computation complexity. Sparse representations in neural networks have emerged as a compelling method to achieve substantial reductions in computational overhead and energy consumption. However, the introduction of sparsity presents challenges such as irregular memory accesses and wasted computation cycles. Traditional methods have attempted to address these challenges with varied success, unfortunately, often involving complex hardware designs or sacrificing sparsity’s benefits. In this article, we propose a software-hardware codesign solution, comprising an offline workload balancing encoding algorithm toward arbitrary sparsity in DNNs and a dedicated Processing Element array–based accelerator with a lightweight switch network. Extensive experiments are conducted to demonstrate the conclusion that our proposal is feasible to support various network structures and a wide range of sparsity ratios. With the encoding algorithm, a 1.16×–2.61× acceleration is achieved compared to the baseline. The system-on-chip measures 7.9 mm 2 , achieving an energy efficiency of 0.7 TOPS/W (dense), 2.1 TOPS/W (at 75% sparsity), and 10.3 TOPS/W (at 99.9% sparsity) at 0.9 V and 1,066-MHz clock frequency.