1 code implementation • 20 Mar 2024 • Yicheng Wu, Xiangde Luo, Zhe Xu, Xiaoqing Guo, Lie Ju, ZongYuan Ge, Wenjun Liao, Jianfei Cai
To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation.
2 code implementations • 5 Jan 2024 • Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatra, Brigid Betz-Stablein, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge
To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).
1 code implementation • 23 Nov 2023 • Peng Xia, Xingtong Yu, Ming Hu, Lie Ju, Zhiyong Wang, Peibo Duan, ZongYuan Ge
We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category.
Fine-Grained Visual Recognition Graph Representation Learning
no code implementations • 28 Sep 2023 • Wei Feng, Lie Ju, Lin Wang, Kaimin Song, ZongYuan Ge
We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories.
1 code implementation • 8 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)
no code implementations • 8 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.
1 code implementation • 4 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.
no code implementations • 11 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.
no code implementations • 16 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.
no code implementations • 13 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.
1 code implementation • 8 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.
no code implementations • 7 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.
no code implementations • 17 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.
no code implementations • 4 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.
1 code implementation • 18 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.
no code implementations • 22 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.
no code implementations • 28 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.
no code implementations • 27 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.
no code implementations • 24 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.
no code implementations • 23 Mar 2020 • Lie Ju, Xin Wang, Quan Zhou, Hu Zhu, Mehrtash Harandi, Paul Bonnington, Tom Drummond, ZongYuan Ge
We design a regularisation technique to regulate the domain adaptation.