6 papers
Region-Grounded Vision-Language Learning for Detection-Guided Mammographic Lesion Classification
Zhengbo Zhou, Jiren Li, Dooman Arefan +2
Vision-language models trained with contrastive objectives have shown promise in medical image analysis. However, conventional global image-text alignment is ill-suited for mammogr…
Diagnostic Performance of Universal-Learning Ultrasound AI Across Multiple Organs and Tasks: the UUSIC25 Challenge
Zehui Lin, Luyi Han, Xin Wang +15
IMPORTANCE: Modern ultrasound systems are universal diagnostic tools capable of imaging the entire body. However, current AI solutions remain fragmented into single-task tools. Thi…
t-Mamba3D: A Time-Aware Spatio-Temporal State-Space Model for Breast Cancer Risk Prediction
Zhengbo Zhou, Dooman Arefan, Margarita Zuley +1
Longitudinal analysis of sequential radiological images is hampered by a fundamental data challenge: how to effectively model a sequence of high-resolution images captured at irreg…
STA-Risk: A Deep Dive of Spatio-Temporal Asymmetries for Breast Cancer Risk Prediction
Zhengbo Zhou, Dooman Arefan, Margarita Zuley +2
Predicting the risk of developing breast cancer is an important clinical tool to guide early intervention and tailoring personalized screening strategies. Early risk models have li…
Longitudinal Mammogram Exam-based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks
Zhengbo Zhou, Degan Hao, Dooman Arefan +3
In breast cancer detection and diagnosis, the longitudinal analysis of mammogram images is crucial. Contemporary models excel in detecting temporal imaging feature changes, thus en…
Adversarially Robust Feature Learning for Breast Cancer Diagnosis
Degan Hao, Dooman Arefan, Margarita Zuley +2
Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are robust to adversarial data while accurate on standa…