Search Results for author: Quan Van Nguyen

Found 6 papers, 4 papers with code

UIT-DarkCow team at ImageCLEFmedical Caption 2024: Diagnostic Captioning for Radiology Images Efficiency with Transformer Models

no code implementations27 May 2024 Quan Van Nguyen, Huy Quang Pham, Dan Quang Tran, Thang Kien-Bao Nguyen, Nhat-Hao Nguyen-Dang, Bao-Thien Nguyen-Tat

Purpose: This study focuses on the development of automated text generation from radiology images, termed diagnostic captioning, to assist medical professionals in reducing clinical errors and improving productivity.

Decoder Text Generation

ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in Images

1 code implementation29 Apr 2024 Huy Quang Pham, Thang Kien-Bao Nguyen, Quan Van Nguyen, Dan Quang Tran, Nghia Hieu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

To this end, we introduce a novel dataset, ViOCRVQA (Vietnamese Optical Character Recognition - Visual Question Answering dataset), consisting of 28, 000+ images and 120, 000+ question-answer pairs.

Optical Character Recognition Optical Character Recognition (OCR) +2

LACFormer: Toward accurate and efficient polyp segmentation

1 code implementation BMVC 2023 Quan Van Nguyen, Mai Nguyen, Thanh Tung Nguyen, Huy Trịnh Quang, Toan Pham Van

The proposed model combines the strengths of Transformers and CNNs along with Laplacian images to overcome the limitations of previous models.

Decoder Image Segmentation +2

LAPFormer: A Light and Accurate Polyp Segmentation Transformer

no code implementations10 Oct 2022 Mai Nguyen, Tung Thanh Bui, Quan Van Nguyen, Thanh Tung Nguyen, Toan Van Pham

Polyp segmentation is still known as a difficult problem due to the large variety of polyp shapes, scanning and labeling modalities.

Decoder feature selection

Online pseudo labeling for polyp segmentation with momentum networks

1 code implementation29 Sep 2022 Toan Pham Van, Linh Bao Doan, Thanh Tung Nguyen, Duc Trung Tran, Quan Van Nguyen, Dinh Viet Sang

In this work, we present a new pseudo labeling strategy that enhances the quality of pseudo labels used for training student networks.

Semantic Segmentation

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