Search Results for author: Zhen Yu

Found 13 papers, 3 papers with code

Progressive trajectory matching for medical dataset distillation

no code implementations20 Mar 2024 Zhen Yu, Yang Liu, Qingchao Chen

To solve these barriers, we propose to design a novel progressive trajectory matching strategy to improve the training stability for medical image dataset distillation.

Transfer Learning

Prompt-driven Latent Domain Generalization for Medical Image Classification

2 code implementations5 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).

Domain Generalization Image Classification +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

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.

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

TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack

1 code implementation20 Jan 2022 Zhen Yu, Xiaosen Wang, Wanxiang Che, Kun He

Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world applications.

Adversarial Attack Hard-label Attack +3

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

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

CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images

no code implementations8 Apr 2020 Youyi Song, Zhen Yu, Teng Zhou, Jeremy Yuen-Chun Teoh, Baiying Lei, Kup-Sze Choi, Jing Qin

Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose.

Image Segmentation Semantic Segmentation

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