11 citations · 23 across the 11 of their papers we have counts for
12 papers
Stochastic Appointment Scheduling with Patient-and-Time-Dependent Probability Distributions
Soheyl Khalilpourazari, Hossein Hashemi Doulabi
Outpatient appointment scheduling must balance provider idle time, overtime, and patient waiting under uncertain service times, punctuality, and attendance. These factors may follo…
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…
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…
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…
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification
Long Gao, Chang Liu, Dooman Arefan +3
Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical sett…
Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images
Long Gao, Chang Liu, Dooman Arefan +2
Medical image data are usually imbalanced across different classes. One-class classification has attracted increasing attention to address the data imbalance problem by distinguish…
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…
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification
Jun Luo, Gene Kitamura, Dooman Arefan +3
Elbow fracture diagnosis often requires patients to take both frontal and lateral views of elbow X-ray radiographs. In this paper, we propose a multiview deep learning method for a…
Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images
Jun Luo, Gene Kitamura, Emine Doganay +2
Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiolo…