13 citations · 14 across the 5 of their papers we have counts for
6 papers
NULLBUS: Multimodal Mixed-Supervision for Breast Ultrasound Segmentation via Nullable Global-Local Prompts
Raja Mallina, Bryar Shareef
Breast ultrasound (BUS) segmentation provides lesion boundaries essential for computer-aided diagnosis and treatment planning. While promptable methods can improve segmentation per…
XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learning
Raja Mallina, Bryar Shareef
Background: Precise breast ultrasound (BUS) segmentation supports reliable measurement, quantitative analysis, and downstream classification, yet remains difficult for small or low…
Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network
Bryar Shareef, Min Xian, Aleksandar Vakanski +1
Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Although convolutional neural networks (CNNs) have demonstrated relia…
SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
Haotian Wang, Min Xian, Aleksandar Vakanski +1
Existing deep neural networks for histopathology image synthesis cannot generate image styles that align with different organs, and cannot produce accurate boundaries of clustered…
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…