most citedBreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images

8 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner

Yuzhang Shang, Bingxin Xu, Weitai Kang +7

Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface l…

cs.MM2024

RoWSFormer: A Robust Watermarking Framework with Swin Transformer for Enhanced Geometric Attack Resilience

Weitong Chen, Yuheng Li

In recent years, digital watermarking techniques based on deep learning have been widely studied. To achieve both imperceptibility and robustness of image watermarks, most current…

cs.CV20242 cited

Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography

Xuxin Chen, Yuheng Li, Mingzhe Hu +5

Although fusion of information from multiple views of mammograms plays an important role to increase accuracy of breast cancer detection, developing multi-view mammograms-based com…

cs.CV20232 cited

Visual Instruction Inversion: Image Editing via Visual Prompting

Thao Nguyen, Yuheng Li, Utkarsh Ojha +1

Text-conditioned image editing has emerged as a powerful tool for editing images. However, in many situations, language can be ambiguous and ineffective in describing specific imag…

eess.IV20238 cited

BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images

Mingzhe Hu, Yuheng Li, Xiaofeng Yang

Breast cancer is one of the most common cancers among women worldwide, with early detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic too…