Research
Stitch-Inferencer: Enhance Endoscopic Video Segmentation and Tracking via Panoramic Reconstruction
Jmees Inc.
Effective surgical video understanding requires the clear decomposition of the surgical scene into foreground elements, such as surgical tools, and background anatomical structures.
This research applies image stitching techniques to surgical videos to enable the automated removal and inpainting of specific objects based on ground truth data.
By generating a stabilized, wide-angle view, we aim to enhance the temporal consistency of model predictions for downstream tasks, including segmentation, tracking, and phase classification.
Furthermore, providing contextual information from outside the current frame allows the model to improve its overall predictive accuracy and robustness.
Related research outputs and applications:
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Cholec80-port (CARS 2026): During the development of Stitch-Inferencer, we created and publicly released a geometrically consistent trocar port segmentation dataset required for the research. The paper was accepted to CARS 2026 and will appear in the IJCARS supplement. Read the paper
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MVAA Challenge 2026 solution (MICCAI 2026 Workshop on Medical World Models): We applied the Stitch-Inferencer pipeline to tool-free view generation as part of our challenge solution. The solution paper was accepted to the workshop. Read the paper
Nov 2025 – Present. Leading the project as the first author. Accepted to the MICCAI 2026 Workshop SafeSurg. Read the paper
Phase Recognition of Laparoscopic Hysterectomy Toward Robotic Automation
National Cancer Center East Hospital, Japan
In total hysterectomy, a uterine manipulator is used to stabilize the uterus and ensure safe resection.
However, during robot-assisted surgery, intraoperative manipulation of the device cannot be controlled directly by the surgeon, highlighting the need for automation.
In this research, we aim to develop a surgical phase classification model that recognizes the manipulator's angle from endoscopic video.
The annotation scheme is being refined through close collaboration and repeated discussion between clinicians and annotators to ensure it is suitable for deep learning models.
April 2025 – Present. Working as a project researcher.
scGRNdb
Dr. Xia Yang's Lab @ UCLA
SCING is a machine learning model that predicts gene regulatory networks from single-cell & spatial transcriptomics data.
Using GRNs from SCING, we are developing scGRNdb, an analysis pipeline with a database of gene regulatory networks for single-cell and spatial transcriptomics data.
I was involved in the part of designing community detection algorithm, pipeline development, and Evaluations.
23 Winter - 25 Spring. Working as an Undergraduate Research Assistant.
Temporal Memory Augmentation Module (TMAM)
Jmees Inc.
TMAM a novel framework that adapts any existing 2D segmentation model for video processing by transferring SAM2's memory encoder and attention modules.
TMAM applies a memory encoder to past-frame predictions and uses memory attention to refine current-frame features. By leveraging the inherent temporal redundancy in video sequences,
TMAM captures contextual cues that may be overlooked by single-frame processing, thereby improving robustness to occlusions and boundary artifacts.
24 Winter - 25 Spring. Leading the project as the first author of the paper.
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Clinical Trial of Potential Migraine Drug on Rodent Model
Dr. Jennifer Xie's Lab @ Arkansas State University
This research proposes to investigate the efficacy of RgIA4, a selective α9α10 nicotinic acetylcholine receptor (nAChR) antagonist,
in alleviating migraine-related pain using rodent models. The study will evaluate RgIA4's dose-
and time-dependent effects in rat and mouse models by inducing migraines through environmental stress and inflammatory mediators.
The research aims to determine whether blocking α9α10 nAChR can reduce pain and inflammatory responses, potentially offering a new therapeutic approach for migraines with fewer side effects and a reduced risk of medication overuse headaches.
21 Fall - 23 Summer. Working as an Undergraduate Research Scholor.
