5 citations · 14 across the 22 of their papers we have counts for
22 papers
Improving Cross-Site Whole-Heart Segmentation
Tanish Mudaliar, Justin Li, Daniel Lin +4
Whole-heart segmentation from CT and MRI is essential for quantitative cardiac image analysis, but remains challenging under multi-center and multi-modality distribution shift. In…
Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agents
Peng Huang, Yiming Wang, Yineng Chen +9
Echocardiography plays an important role in the screening and diagnosis of cardiovascular diseases. However, automated intelligent analysis of echocardiographic data remains challe…
Robust Multi-Source Covid-19 Detection in CT Images
Asmita Yuki Pritha, Jason Xu, Daniel Ding +4
Deep learning models for COVID-19 detection from chest CT scans generally perform well when the training and test data originate from the same institution, but they often struggle…
SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMs
Shail Desai, Aditya Pawar, Li Lin +2
Artificial Intelligence (AI) has made it possible for anyone to create images, audio, and video with unprecedented ease, enriching education, communication, and creative expression…
MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models
Philip R. Liu, Sparsh Bansal, Jimmy Dinh +6
The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologist shortages and improve clinica…
Preserving AUC Fairness in Learning with Noisy Protected Groups
Mingyang Wu, Li Lin, Wenbin Zhang +3
The Area Under the ROC Curve (AUC) is a key metric for classification, especially under class imbalance, with growing research focus on optimizing AUC over accuracy in applications…
Improving Generalization of Medical Image Registration Foundation Model
Jing Hu, Kaiwei Yu, Hongjiang Xian +2
Deformable registration is a fundamental task in medical image processing, aiming to achieve precise alignment by establishing nonlinear correspondences between images. Traditional…
RLMiniStyler: Light-weight RL Style Agent for Arbitrary Sequential Neural Style Generation
Jing Hu, Chengming Feng, Shu Hu +4
Arbitrary style transfer aims to apply the style of any given artistic image to another content image. Still, existing deep learning-based methods often require significant computa…
Robust Fairness Vision-Language Learning for Medical Image Analysis
Sparsh Bansal, Mingyang Wu, Xin Wang +1
The advent of Vision-Language Models (VLMs) in medical image analysis has the potential to help process multimodal inputs and increase performance over traditional inference method…
Robust AI-Generated Face Detection with Imbalanced Data
Yamini Sri Krubha, Aryana Hou, Braden Vester +4
Deepfakes, created using advanced AI techniques such as Variational Autoencoder and Generative Adversarial Networks, have evolved from research and entertainment applications into…
Diffusion-empowered AutoPrompt MedSAM
Peng Huang, Shu Hu, Bo Peng +5
MedSAM, a medical foundation model derived from the SAM architecture, has demonstrated notable success across diverse medical domains. However, its clinical application faces two m…
Improving Generalization for AI-Synthesized Voice Detection
Hainan Ren, Li Lin, Chun-Hao Liu +2
AI-synthesized voice technology has the potential to create realistic human voices for beneficial applications, but it can also be misused for malicious purposes. While existing AI…
An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentation
Ting Yu Tsai, Liangqiao Gui, Yineng Chen +8
Deep learning models have achieved significant success in segmenting cardiovascular structures, but there is a growing need to improve their generalization and robustness. Current…
Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation
Xin Wang, Xiaoyu Liu, Peng Huang +3
Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions rem…
AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark
Li Lin, Santosh, Mingyang Wu +2
AI-generated faces have enriched human life, such as entertainment, education, and art. However, they also pose misuse risks. Therefore, detecting AI-generated faces becomes crucia…
UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation
Ting Yu Tsai, Li Lin, Shu Hu +3
Biomedical image segmentation is critical for accurate identification and analysis of anatomical structures in medical imaging, particularly in cardiac MRI. Manual segmentation is…
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Santosh, Li Lin, Irene Amerini +2
Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. However, their ability to create hyper-realist…
Robust Light-Weight Facial Affective Behavior Recognition with CLIP
Li Lin, Sarah Papabathini, Xin Wang +1
Human affective behavior analysis aims to delve into human expressions and behaviors to deepen our understanding of human emotions. Basic expression categories (EXPR) and Action Un…
Robust COVID-19 Detection in CT Images with CLIP
Li Lin, Yamini Sri Krubha, Zhenhuan Yang +5
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources,…
Preserving Fairness Generalization in Deepfake Detection
Li Lin, Xinan He, Yan Ju +3
Although effective deepfake detection models have been developed in recent years, recent studies have revealed that these models can result in unfair performance disparities among…
Detecting Multimedia Generated by Large AI Models: A Survey
Li Lin, Neeraj Gupta, Yue Zhang +7
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integr…
Uncertainty-Aware Explainable Recommendation with Large Language Models
Yicui Peng, Hao Chen, Chingsheng Lin +7
Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tai…