PROJECTS

UU-Mamba is an uncertainty-aware segmentation framework designed for reliable cardiovascular MRI analysis. It integrates the Mamba selective state space model into a U-Net–style architecture to capture both local anatomical features and long-range spatial dependencies with high computational efficiency. By incorporating uncertainty estimation into the training process, the model improves robustness and reliability, particularly in challenging anatomical regions, enabling more stable and clinically meaningful segmentation results.

The paper can be accessed at: UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation.

IntelliCardiac is a full-stack medical imaging web platform designed for cardiac segmentation and disease classification using cine MRI.

Combining a deep learning-based segmentation model with a two-step classification pipeline. The segmentation module gains an overall accuracy of 92.6%. The classification module, trained on characteristics taken from segmented heart structures, achieves 98% accuracy in five categories.

These results outperform current state-of-the-art systems that integrate both segmentation and classification. Supporting real-time visualization, workflow integration, and AI-assisted diagnostics, IntelliCardiac shows strong promise as a scalable, accurate tool for clinical decision-making in cardiac imaging.

The paper can be accessed at: IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification.

The precise identification and categorization of seizure stages through observable behaviors is essential for understanding and diagnosing neurological disorders. Traditional studies in neurology rely heavily on manual observation, which is both time-intensive and requires considerable manpower.

Utilizing rodent models for epilepsy research offers a pathway to discovering more effective drugs or neurotherapy treatments. The preliminary screening of potential anti-epileptic drugs (AED) with these models necessitates extensive video surveillance, a process that is not only slow and subjective but also prone to mistakes.

This research aims to introduce an AI-powered method for the quantitative analysis of rodent behaviors, including the classification of epilepsy stages. By employing advanced deep learning and computer vision technologies, we created an automated system for detecting and recognizing seizures in animals. A novel hybrid technique, merging model-based and data-driven approaches, was crafted to automate the classification of epilepsy stages from video footage.

This investigation validates the practicality of using video analysis for detecting and classifying seizure stages in rodent models of temporal lobe seizures, offering a consistent and quantitative way to study rodent behavior. This could potentially aid further behavioral studies in animals, such as those related to depression and anxiety. In the future, we plan to include and share a public dataset with the research community.

ReDiPrune is a training-free visual token pruning method for improving the efficiency of multimodal large language models. Our key idea is to prune tokens before the vision-language projector, where visual features are still rich and discriminative, instead of pruning after projection on compressed representations. To preserve both usefulness and coverage, we score tokens with a lightweight rule that jointly considers text-conditioned relevance and max-min diversity, so the selected tokens are both query-relevant and non-redundant. ReDiPrune is fully plug-and-play, requires no retraining or architectural changes, and can be seamlessly integrated into existing MLLM pipelines.