Search Results for author: ZongYuan Ge

Found 33 papers, 10 papers with code

Medical Visual Question Answering: A Survey

no code implementations19 Nov 2021 Zhihong Lin, Donghao Zhang, Qingyi Tac, Danli Shi, Gholamreza Haffari, Qi Wu, Mingguang He, ZongYuan Ge

Medical Visual Question Answering (VQA) is a combination of medical artificial intelligence and popular VQA challenges.

Medical Visual Question Answering Question Answering +1

Long-Tailed Multi-Label Retinal Diseases Recognition Using Hierarchical Information and Hybrid Knowledge Distillation

no code implementations17 Nov 2021 Lie Ju, Xin Wang, Zhen Yu, Lin Wang, Xin Zhao, ZongYuan Ge

In the real world, medical datasets often exhibit a long-tailed data distribution (i. e., a few classes occupy most of the data, while most classes have rarely few samples), which results in a challenging imbalance learning scenario.

Knowledge Distillation

Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks

no code implementations21 Oct 2021 ZongYuan Ge, Xin Wang

The current generation of deep neural networks has achieved close-to-human results on "closed-set" image recognition; that is, the classes being evaluated overlap with the training classes.

Decision Making Open Set Learning

Early Melanoma Diagnosis with Sequential Dermoscopic Images

no code implementations12 Oct 2021 Zhen Yu, Jennifer Nguyen, Toan D Nguyen, John Kelly, Catriona Mclean, Paul Bonnington, Lei Zhang, Victoria Mar, ZongYuan Ge

In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images.

IDENTIFYING CONCEALED OBJECTS FROM VIDEOS

no code implementations29 Sep 2021 Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames.

Object Detection

Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation

1 code implementation21 Sep 2021 Yicheng Wu, ZongYuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong Xia, Jianfei Cai

In this paper, we proposed a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled hard regions for semi-supervised medical image segmentation.

Medical Image Segmentation

Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization

no code implementations4 Aug 2021 Wei Feng, Lie Ju, Lin Wang, Kaimin Song, Xin Wang, Xin Zhao, Qingyi Tao, ZongYuan Ge

In this work, we explore unsupervised domain adaptation in retinal vessel segmentation by using entropy-based adversarial learning and transfer normalization layer to train a segmentation network, which generalizes well across domains and requires no annotation of the target domain.

Retinal Vessel Segmentation Unsupervised Domain Adaptation

Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

1 code implementation18 Jun 2021 Lin Wang, Lie Ju, Donghao Zhang, Xin Wang, Wanji He, Yelin Huang, Zhiwen Yang, Xuan Yao, Xin Zhao, Xiufen Ye, ZongYuan Ge

Secondly, the matting method applied to the natural image is not suitable for the medical scene, so we propose a new architecture to generate binary masks and alpha matte in a row.

Image Matting Medical Image Segmentation

Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition

no code implementations22 Apr 2021 Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, ZongYuan Ge

For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models.

Knowledge Distillation

Semi-supervised Left Atrium Segmentation with Mutual Consistency Training

2 code implementations4 Mar 2021 Yicheng Wu, Minfeng Xu, ZongYuan Ge, Jianfei Cai, Lei Zhang

Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions.

Left Atrium Segmentation Medical Image Segmentation

Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation

no code implementations28 Feb 2021 Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Mehrtash Harandi, Tom Drummond, Tongliang Liu, ZongYuan Ge

In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from wrong diagnosis record.

General Classification Image Classification +1

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement

1 code implementation NeurIPS 2020 Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, ZongYuan Ge, Steven Su

A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search.

bilevel optimization Neural Architecture Search

Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling

no code implementations27 Nov 2020 Lie Ju, Xin Wang, Xin Zhao, Paul Bonnington, Tom Drummond, ZongYuan Ge

We propose the use of a modified cycle generative adversarial network (CycleGAN) model to bridge the gap between regular and UWF fundus and generate additional UWF fundus images for training.

Hierarchical Neural Architecture Search for Deep Stereo Matching

1 code implementation NeurIPS 2020 Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Tom Drummond, Hongdong Li, ZongYuan Ge

To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation.

Neural Architecture Search Semantic Segmentation +2

Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning

no code implementations24 Mar 2020 Lie Ju, Xin Wang, Xin Zhao, Huimin Lu, Dwarikanath Mahapatra, Paul Bonnington, ZongYuan Ge

In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.

Classification General Classification +2

ZSTAD: Zero-Shot Temporal Activity Detection

no code implementations CVPR 2020 Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Sen Wang, ZongYuan Ge, Alexander Hauptmann

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos.

Action Detection Activity Detection

Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation

1 code implementation11 Oct 2019 Yunyan Xing, ZongYuan Ge, Rui Zeng, Dwarikanath Mahapatra, Jarrel Seah, Meng Law, Tom Drummond

We demonstrate the effectiveness of our model on two tasks: (i) we invite certified radiologists to assess the quality of the generated synthetic images against real and other state-of-the-art generative models, and (ii) data augmentation to improve the performance of disease localisation.

Data Augmentation Image-to-Image Translation +1

Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention

1 code implementation9 Jul 2019 Qingyi Tao, ZongYuan Ge, Jianfei Cai, Jianxiong Yin, Simon See

Secondly, in CT scans, the lesions are often indistinguishable from the background since the lesion and non-lesion areas may have very similar appearances.

Computed Tomography (CT) Object Detection

Geometry-constrained Car Recognition Using a 3D Perspective Network

no code implementations19 Mar 2019 Rui Zeng, ZongYuan Ge, Simon Denman, Sridha Sridharan, Clinton Fookes

Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category.

Chest X-rays Classification: A Multi-Label and Fine-Grained Problem

no code implementations19 Jul 2018 Zongyuan Ge, Dwarikanath Mahapatra, Suman Sedai, Rahil Garnavi, Rajib Chakravorty

In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data.

General Classification Image Classification

Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies

1 code implementation18 Sep 2017 Fangyi Zhang, Jürgen Leitner, ZongYuan Ge, Michael Milford, Peter Corke

Policies can be transferred to real environments with only 93 labelled and 186 unlabelled real images.

Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification

no code implementations1 Aug 2016 ZongYuan Ge, Chris McCool, Conrad Sanderson, Peng Wang, Lingqiao Liu, Ian Reid, Peter Corke

Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification.

Classification General Classification +1

Simple Online and Realtime Tracking

48 code implementations2 Feb 2016 Alex Bewley, ZongYuan Ge, Lionel Ott, Fabio Ramos, Ben Upcroft

This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.

Multiple Object Tracking

Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks

no code implementations30 Nov 2015 ZongYuan Ge, Alex Bewley, Christopher Mccool, Ben Upcroft, Peter Corke, Conrad Sanderson

We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN).

Classification Fine-Grained Image Classification +1

Subset Feature Learning for Fine-Grained Category Classification

no code implementations9 May 2015 Zongyuan Ge, Christopher Mccool, Conrad Sanderson, Peter Corke

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images.

Classification General Classification +1

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