29 citations · 49 across the 6 of their papers we have counts for
6 papers
GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights
Qiming He, Jing Li, Tian Guan +26
Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging…
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
Gongbo Liang, Connor Greenwell, Yu Zhang +3
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, tex…
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification
Gongbo Liang, Yu Zhang, Xiaoqin Wang +1
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these wo…
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
Yu Zhang, Gongbo Liang, Nathan Jacobs +1
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on pub…
Joint 2D-3D Breast Cancer Classification
Gongbo Liang, Xiaoqin Wang, Yu Zhang +4
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D…
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification
Yu Zhang, Xiaoqin Wang, Hunter Blanton +3
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical pr…