1 code implementation • 29 Mar 2024 • Yan Luo, Min Shi, Muhammad Osama Khan, Muhammad Muneeb Afzal, Hao Huang, Shuaihang Yuan, Yu Tian, Luo Song, Ava Kouhana, Tobias Elze, Yi Fang, Mengyu Wang
Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions.
1 code implementation • NeurIPS 2023 • Mengyu Wang, Henghui Ding, Jun Hao Liew, Jiajun Liu, Yao Zhao, Yunchao Wei
We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process.
1 code implementation • 3 Nov 2023 • Yu Tian, Min Shi, Yan Luo, Ava Kouhana, Tobias Elze, Mengyu Wang
Existing medical fairness datasets are all for classification tasks, and no fairness datasets are available for medical segmentation, while medical segmentation is an equally important clinical task as classifications, which can provide detailed spatial information on organ abnormalities ready to be assessed by clinicians.
no code implementations • 3 Oct 2023 • Yan Luo, Muhammad Osama Khan, Yu Tian, Min Shi, Zehao Dou, Tobias Elze, Yi Fang, Mengyu Wang
To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes.
no code implementations • ICCV 2023 • Yan Luo, Min Shi, Yu Tian, Tobias Elze, Mengyu Wang
This is the largest glaucoma detection dataset with 3D OCT imaging data and the first glaucoma progression forecasting dataset that is publicly available.
1 code implementation • 15 Jun 2023 • Yan Luo, Yu Tian, Min Shi, Louis R. Pasquale, Lucy Q. Shen, Nazlee Zebardast, Tobias Elze, Mengyu Wang
To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection.
1 code implementation • 2 Sep 2022 • Min Shi, Anagha Lokhande, Mojtaba S. Fazli, Vishal Sharma, Yu Tian, Yan Luo, Louis R. Pasquale, Tobias Elze, Michael V. Boland, Nazlee Zebardast, David S. Friedman, Lucy Q. Shen, Mengyu Wang
Ophthalmic images and derivatives such as the retinal nerve fiber layer (RNFL) thickness map are crucial for detecting and monitoring ophthalmic diseases (e. g., glaucoma).
1 code implementation • 9 May 2022 • Mohammad Eslami, Solale Tabarestani, Ehsan Adeli, Glyn Elwyn, Tobias Elze, Mengyu Wang, Nazlee Zebardast, Nassir Navab, Malek Adjouadi
With the advent of sophisticated machine learning (ML) techniques and the promising results they yield, especially in medical applications, where they have been investigated for different tasks to enhance the decision-making process.
1 code implementation • 22 Mar 2022 • Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W Verjans, Mengyu Wang, Gustavo Carneiro
Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder.
no code implementations • 29 Sep 2021 • Mengyu Wang, Yijia Shao, Haowei Lin, Wenpeng Hu, Bing Liu
Recently, contrastive loss with data augmentation and pseudo class creation has been shown to produce markedly better results for out-of-distribution (OOD) detection than previous methods.
1 code implementation • 6 Aug 2021 • Xuejiao Tang, Wenbin Zhang, Yi Yu, Kea Turner, Tyler Derr, Mengyu Wang, Eirini Ntoutsi
While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge.
no code implementations • ICCV 2021 • Jiaheng Liu, Yudong Wu, Yichao Wu, Chuming Li, Xiaolin Hu, Ding Liang, Mengyu Wang
To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.
1 code implementation • NeurIPS 2020 • Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu
Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data.
1 code implementation • COLING 2020 • Wenpeng Hu, Mengyu Wang, Bing Liu, Feng Ji, Haiqing Chen, Dongyan Zhao, Jinwen Ma, Rui Yan
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.