12 citations · 17 across the 6 of their papers we have counts for
6 papers
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection
Weijie He, Runyuan Bao, Yiru Cang +3
This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis. The authors propose a novel Tra…
Polyper: Boundary Sensitive Polyp Segmentation
Hao Shao, Yang Zhang, Qibin Hou
We present a new boundary sensitive framework for polyp segmentation, called Polyper. Our method is motivated by a clinical approach that seasoned medical practitioners often lever…
Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains
Yang Zhang, Huilin Pan, Mingying Li +3
Despite the successful application of convolutional neural networks (CNNs) in object detection tasks, their efficiency in detecting faults from freight train images remains inadequ…
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation
An Wang, Mobarakol Islam, Mengya Xu +2
The Segment Anything Model (SAM) serves as a fundamental model for semantic segmentation and demonstrates remarkable generalization capabilities across a wide range of downstream s…
SME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
An Wang, Mengya Xu, Yang Zhang +2
Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions f…
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective
An Wang, Mobarakol Islam, Mengya Xu +2
Segment Anything Model (SAM) is a foundation model for semantic segmentation and shows excellent generalization capability with the prompts. In this empirical study, we investigate…