Mengyu Wang
Orcid: 0000-0002-7188-7126Affiliations:
- Schepens Eye Research Institute, Harvard Ophthalmology AI Lab, Boston, MA, USA
- Harvard Medical School, Massachusetts Eye and Ear, Boston, MA, USA
- Duke University School of Medicine, Durham, NC, USA (former)
According to our database1,
Mengyu Wang
authored at least 13 papers
between 2016 and 2024.
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Bibliography
2024
2023
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma.
IEEE J. Biomed. Health Informatics, September, 2023
FairSeg: A Large-scale Medical Image Segmentation Dataset for Fairness Learning with Fair Error-Bound Scaling.
CoRR, 2023
Harvard Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling.
CoRR, 2023
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization.
CoRR, 2023
Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder.
Proceedings of the Machine Learning in Medical Imaging - 14th International Workshop, 2023
Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
2022
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images.
CoRR, 2022
Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning.
CoRR, 2022
2020
Patterns of retinal nerve fiber layer loss in patients with glaucoma identified by deep archetypal analysis.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020
2016
Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.
Expert Syst. Appl., 2016
A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases.
Expert Syst. Appl., 2016
Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features.
Proceedings of the Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, San Diego, California, United States, 27 February, 2016