Search Results for author: Zhiqiang He

Found 20 papers, 9 papers with code

Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective

1 code implementation29 Dec 2021 Lanning Wei, Huan Zhao, Zhiqiang He

To enjoy the benefits while alleviating the corresponding deficiencies of these two manners, we learn to design the topology of GNNs in a novel feature fusion perspective which is dubbed F$^2$GNN.

Neural Architecture Search

Learn Layer-wise Connections in Graph Neural Networks

no code implementations27 Dec 2021 Lanning Wei, Huan Zhao, Zhiqiang He

In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse applications on real-world datasets.

Neural Architecture Search

A Survey of Visual Transformers

1 code implementation11 Nov 2021 Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao shi, Jianping Fan, Zhiqiang He

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).

Pooling Architecture Search for Graph Classification

1 code implementation24 Aug 2021 Lanning Wei, Huan Zhao, Quanming Yao, Zhiqiang He

To address this problem, we propose to use neural architecture search (NAS) to search for adaptive pooling architectures for graph classification.

Classification Graph Classification +1

TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation

1 code implementation21 Jul 2021 Jiawei Yang, Yao Zhang, Yuan Liang, Yang Zhang, Lei He, Zhiqiang He

Experiments on kidney tumor segmentation task demonstrate that TumorCP surpasses the strong baseline by a remarkable margin of 7. 12% on tumor Dice.

Data Augmentation Tumor Segmentation

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

1 code implementation28 Jun 2021 Yixin Wang, Yang Zhang, Yang Liu, Zihao Lin, Jiang Tian, Cheng Zhong, Zhongchao shi, Jianping Fan, Zhiqiang He

Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.

Brain Tumor Segmentation Transfer Learning +1

Trust It or Not: Confidence-Guided Automatic Radiology Report Generation

no code implementations21 Jun 2021 Yixin Wang, Zihao Lin, Zhe Xu, Haoyu Dong, Jiang Tian, Jie Luo, Zhongchao shi, Yang Zhang, Jianping Fan, Zhiqiang He

Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.

Decision Making Image Captioning

Efficient Graph Neural Architecture Search

no code implementations1 Jan 2021 Huan Zhao, Lanning Wei, Quanming Yao, Zhiqiang He

To obtain state-of-the-art (SOAT) data-specific GNN architectures, researchers turn to the neural architecture search (NAS) methods.

Neural Architecture Search Transfer Learning

Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer

1 code implementation29 Dec 2020 Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Yang Zhang, Zhiqiang He

Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice.

Semantic Segmentation Style Transfer

Double-Uncertainty Weighted Method for Semi-supervised Learning

no code implementations19 Oct 2020 Yixin Wang, Yao Zhang, Jiang Tian, Cheng Zhong, Zhongchao shi, Yang Zhang, Zhiqiang He

We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i. e. segmentation uncertainty and feature uncertainty.

Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database

no code implementations1 Nov 2019 Yao Zhang, Cheng Zhong, Yang Zhang, Zhongchao shi, Zhiqiang He

In the SFAN, a Semantic Attention Transmission (SAT) module is designed to select discriminative low-level localization details with the guidance of neighboring high-level semantic information.

Computed Tomography (CT) Tumor Segmentation

Image Co-segmentation via Multi-scale Local Shape Transfer

no code implementations15 May 2018 Wei Teng, Yu Zhang, Xiaowu Chen, Jia Li, Zhiqiang He

Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category.

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