16 citations · 22 across the 6 of their papers we have counts for
7 papers
MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis
Di Dai, Bo Liu, Youcheng Li +9
Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality mammography datasets for AI…
UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
Jigang Fan, Quanlin Wu, Shengjie Luo +1
The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and e…
A Foundational Generative Model for Breast Ultrasound Image Analysis
Haojun Yu, Youcheng Li, Nan Zhang +20
Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development to breast ultrasound analysis remains unt…
Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses
Haojun Yu, Youcheng Li, Nan Zhang +17
Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail…
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection
Haojun Yu, Youcheng Li, QuanLin Wu +4
During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexp…
Denoising Masked AutoEncoders Help Robust Classification
Quanlin Wu, Hang Ye, Yuntian Gu +3
In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corru…
Guided Diffusion Model for Adversarial Purification from Random Noise
Quanlin Wu, Hang Ye, Yuntian Gu
In this paper, we propose a novel guided diffusion purification approach to provide a strong defense against adversarial attacks. Our model achieves 89.62% robust accuracy under PG…