245 citations · 870 across the 16 of their papers we have counts for
8 papers · 1 filter
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer
Shoukun Sun, Min Xian, Aleksandar Vakanski +1
Robust self-training (RST) can augment the adversarial robustness of image classification models without significantly sacrificing models' generalizability. However, RST and other…
EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer
Jiaqiao Shi, Aleksandar Vakanski, Min Xian +2
Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impede…
Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis
Sujata Butte, Haotian Wang, Min Xian +1
Existing deep learning-based approaches for histopathology image analysis require large annotated training sets to achieve good performance; but annotating histopathology images is…
TA-Net: Topology-Aware Network for Gland Segmentation
Haotian Wang, Min Xian, Aleksandar Vakanski
Gland segmentation is a critical step to quantitatively assess the morphology of glands in histopathology image analysis. However, it is challenging to separate densely clustered g…
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
Bryar Shareef, Alex Vakanski, Min Xian +1
Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for…
Stan: Small tumor-aware network for breast ultrasound image segmentation
Bryar Shareef, Min Xian, Aleksandar Vakanski
Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been propose…
Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images
Haotian Wang, Min Xian, Aleksandar Vakanski
Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on public datasets; ho…
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images
Aleksandar Vakanski, Min Xian, Phoebe Freer
Incorporating human domain knowledge for breast tumor diagnosis is challenging, since shape, boundary, curvature, intensity, or other common medical priors vary significantly acros…