Search Results for author: Mengyu Wang

Found 16 papers, 12 papers with code

FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

1 code implementation11 Jul 2024 Yu Tian, Congcong Wen, Min Shi, Muhammad Muneeb Afzal, Hao Huang, Muhammad Osama Khan, Yan Luo, Yi Fang, Mengyu Wang

However, the fairness issue under the setting of domain transfer is almost unexplored, while it is common that clinics rely on different imaging technologies (e. g., different retinal imaging modalities) for patient diagnosis.

Domain Adaptation Fairness +4

FairDiff: Fair Segmentation with Point-Image Diffusion

1 code implementation8 Jul 2024 Wenyi Li, Haoran Xu, Guiyu Zhang, Huan-ang Gao, Mingju Gao, Mengyu Wang, Hao Zhao

Fairness is an important topic for medical image analysis, driven by the challenge of unbalanced training data among diverse target groups and the societal demand for equitable medical quality.

Fairness Image Generation

FairCLIP: Harnessing Fairness in Vision-Language Learning

1 code implementation CVPR 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.


SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process

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.

Denoising Dichotomous Image Segmentation +4

FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

1 code implementation3 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.

Fairness Image Segmentation +3

FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling

no code implementations3 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.


Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning

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.


Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

1 code implementation15 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.

Fairness Feature Importance

Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning

1 code implementation9 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.

Decision Making Memorization +1

Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

1 code implementation22 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.

Image Reconstruction Unsupervised Anomaly Detection

Efficient Out-of-Distribution Detection via CVAE data Generation

no code implementations29 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.

Data Augmentation Out-of-Distribution Detection +1

Interpretable Visual Understanding with Cognitive Attention Network

1 code implementation6 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.

Scene Understanding Visual Commonsense Reasoning

DAM: Discrepancy Alignment Metric for Face Recognition

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.

Face Recognition

HRN: A Holistic Approach to One Class Learning

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.

Anomaly Detection Image Classification

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