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- Beng Koon NgSuggested from co-authorship
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Works11 from public data
- Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences43
This work proposes to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions.
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- Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation13
A simple semi-supervised learning method for lesion segmentation tasks based on the ideas of cut-paste augmentation and consistency regularization is presented, which achieves consistent and superior performance over other self-training and consistency-based methods without introducing sophisticated network components.
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- Coarse-to-fine visual representation learning for medical images via class activation maps5
The value of coarsely labeled datasets in learning transferable representations for medical images is investigated and CAMContrast, a two-stage representation learning framework for medical images, is proposed.
- Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images4
A deep learning system that estimates vCDR from standard, UWF, and smartphone-based images and may be used as a general and interpretable screening tool to improve community reach for diagnosis and management of glaucoma is developed.
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- Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System–
An AI-based automated VA testing system integrating artificial intelligence (AI)–driven speech and image recognition technologies, enabling self-administered, clinic-based VA assessment, supporting its feasibility for clinical implementation is validated.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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