11 citations · 20 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Human not in the loop: objective sample difficulty measures for Curriculum Learning
Zhengbo Zhou, Jun Luo, Dooman Arefan +2
Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures…
Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis
Jun Luo, Dooman Arefan, Margarita Zuley +2
Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succee…