Search Results for author: ZongYuan Ge

Found 55 papers, 20 papers with code

LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition

1 code implementation8 May 2023 Peng Xia, Di Xu, Lie Ju, Ming Hu, Jun Chen, ZongYuan Ge

Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution.

 Ranked #1 on Long-tail Learning on COCO-MLT (using extra training data)

Long-tail Learning

TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification

no code implementations1 May 2023 Litao Yang, Deval Mehta, Sidong Liu, Dwarikanath Mahapatra, Antonio Di Ieva, ZongYuan Ge

Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI.

Image Classification Multiple Instance Learning +1

Towards Open-Scenario Semi-supervised Medical Image Classification

no code implementations8 Apr 2023 Lie Ju, Yicheng Wu, Wei Feng, Zhen Yu, Lin Wang, Zhuoting Zhu, ZongYuan Ge

Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification.

Domain Adaptation Image Classification +1

EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition

1 code implementation4 Apr 2023 Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatrainst, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge

Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset.

Domain Generalization General Knowledge

Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision

no code implementations CVPR 2023 Siyuan Yan, Zhen Yu, Xuelin Zhang, Dwarikanath Mahapatra, Shekhar S. Chandra, Monika Janda, Peter Soyer, ZongYuan Ge

We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen.

Decision Making

Multimorbidity Content-Based Medical Image Retrieval Using Proxies

no code implementations22 Nov 2022 Yunyan Xing, Benjamin J. Meyer, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

Medical imaging data, such as radiology images, are often multimorbidity; a single sample may have more than one pathology present.

Content-Based Image Retrieval Decision Making +3

3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography

no code implementations11 Oct 2022 Lin Wang, Xiufen Ye, Donghao Zhang, Wanji He, Lie Ju, Yi Luo, Huan Luo, Xin Wang, Wei Feng, Kaimin Song, Xin Zhao, ZongYuan Ge

In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i. e., a soft mask, to describe lesions in a 3D medical image.

Binarization Image Matting

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

no code implementations16 Sep 2022 Lin Wang, Xiufen Ye, Donghao Zhang, Wanji He, Lie Ju, Xin Wang, Wei Feng, Kaimin Song, Xin Zhao, ZongYuan Ge

It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation.

Anatomy Image Matting

Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings

no code implementations13 Sep 2022 Zhen Yu, Toan Nguyen, Yaniv Gal, Lie Ju, Shekhar S. Chandra, Lei Zhang, Paul Bonnington, Victoria Mar, Zhiyong Wang, ZongYuan Ge

Accordingly, the learned prototypes preserve the semantic class relations in the embedding space and we can predict the label of an image by assigning its feature to the nearest hyperbolic class prototype.

T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up

1 code implementation18 Aug 2022 Deyin Liu, Lin Wu, Bo Li, ZongYuan Ge

Our architecture is orthogonal to StackGAN++ , and focuses on person image generation, with all of them together to enrich the spectrum of GANs for the image generation task.

Text-to-Image Generation

Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation

no code implementations17 Aug 2022 Litao Yang, Deval Mehta, Dwarikanath Mahapatra, ZongYuan Ge

Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.

Classification Knowledge Distillation

Deep Laparoscopic Stereo Matching with Transformers

1 code implementation25 Jul 2022 Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Tom Drummond, Zhiyong Wang, ZongYuan Ge

The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection.

object-detection Object Detection +2

Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification

no code implementations9 Jul 2022 Lin Wu, Deyin Liu, Wenying Zhang, Dapeng Chen, ZongYuan Ge, Farid Boussaid, Mohammed Bennamoun, Jialie Shen

In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.

Unsupervised Person Re-Identification

Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization

1 code implementation8 Jul 2022 Wei Feng, Lin Wang, Lie Ju, Xin Zhao, Xin Wang, Xiaoyu Shi, ZongYuan Ge

Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks.

Image Segmentation Semantic Segmentation +1

Camera Adaptation for Fundus-Image-Based CVD Risk Estimation

1 code implementation18 Jun 2022 Zhihong Lin, Danli Shi, Donghao Zhang, Xianwen Shang, Mingguang He, ZongYuan Ge

Most high-quality retinography databases ready for research are collected from high-end fundus cameras, and there is a significant domain discrepancy between different cameras.

Flexible Sampling for Long-tailed Skin Lesion Classification

no code implementations7 Apr 2022 Lie Ju, Yicheng Wu, Lin Wang, Zhen Yu, Xin Zhao, Xin Wang, Paul Bonnington, ZongYuan Ge

To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task.

Classification Lesion Classification +1

Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization

1 code implementation CVPR 2022 Wei Dong, Junsheng Wu, Yi Luo, ZongYuan Ge, Peng Wang

In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing.

Node Classification Representation Learning

Implicit Motion Handling for Video Camouflaged Object Detection

1 code implementation CVPR 2022 Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames.

Motion Estimation object-detection +2

Label uncertainty-guided multi-stream model for disease screening

no code implementations28 Jan 2022 Chi Liu, ZongYuan Ge, Mingguang He, Xiaotong Han

The main idea is dividing the images into simple and hard cases by uncertainty information, and then developing a multi-stream network to deal with different cases separately.

Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding

no code implementations27 Jan 2022 Sourya Dipta Das, Saikat Dutta, Nisarg A. Shah, Dwarikanath Mahapatra, ZongYuan Ge

Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images.

Anomaly Detection

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

Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition

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

From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where only a few available samples are presented for training.

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.

Melanoma Diagnosis


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 Object Detection

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

2 code implementations21 Sep 2021 Yicheng Wu, ZongYuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong Xia, Jianfei Cai

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

Image Segmentation Semantic Segmentation +1

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, Xin Wang, Wanji He, Donghao Zhang, Yelin Huang, Zhiwen Yang, Xuan Yao, Xin Zhao, Xiufen Ye, ZongYuan Ge

None of them investigate the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure.

Image Matting Image Segmentation +2

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

3 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.

Image Segmentation Left Atrium Segmentation +3

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.

Benchmarking General Classification +3

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.

Lesion Detection

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 +3

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) Lesion Detection +2

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 +1

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

Simple Online and Realtime Tracking

56 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.

Multi-Object Tracking 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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