activity
20092021
most citedBiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture

6 citations · 25 across the 10 of their papers we have counts for

collaborators

15 papers

cs.CV20214 cited

PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images

Haozhe Jia, Haoteng Tang, Guixiang Ma +4

Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of CO…

cs.CV2021

Boundary-aware Graph Reasoning for Semantic Segmentation

Haoteng Tang, Haozhe Jia, Weidong Cai +3

In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation. Rather than directly construct the graph…

eess.IV20211 cited

BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation

Xinyi Wang, Tiange Xiang, Chaoyi Zhang +4

The recurrent mechanism has recently been introduced into U-Net in various medical image segmentation tasks. Existing studies have focused on promoting network recursion via reusin…

eess.IV20201 cited

H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task

Haozhe Jia, Weidong Cai, Heng Huang +1

In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net uses the single and cascade…

cs.LG20203 cited

Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data

Bin Gu, Zhiyuan Dang, Xiang Li +1

In a lot of real-world data mining and machine learning applications, data are provided by multiple providers and each maintains private records of different feature sets about com…

cs.CV20206 cited

BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture

Tiange Xiang, Chaoyi Zhang, Dongnan Liu +3

U-Net has become one of the state-of-the-art deep learning-based approaches for modern computer vision tasks such as semantic segmentation, super resolution, image denoising, and i…