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Longitudinal Mammogram Exam-based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks

Autor: Zhou, Zhengbo, Hao, Degan, Arefan, Dooman, Zuley, Margarita, Sumkin, Jules, Wu, Shandong

In breast cancer detection and diagnosis, the longitudinal analysis of mammogram images is crucial. Contemporary models excel in detecting temporal imaging feature changes, thus enhancing the learning process over sequential imaging exams. Yet, the r

Externí odkaz: http://arxiv.org/abs/2411.00837

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Adversarially Robust Feature Learning for Breast Cancer Diagnosis

Autor: Hao, Degan, Arefan, Dooman, Zuley, Margarita, Berg, Wendie, Wu, Shandong

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 standard, clean data. In this study, we proposed a novel adversarially robus

Externí odkaz: http://arxiv.org/abs/2402.08768

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Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention

Autor: Hao, Degan, Negahdar, Mohammadreza

Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-term complications in COVID-19

Externí odkaz: http://arxiv.org/abs/2307.04770

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