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Your search keyword '"Kha, Quang-Hien"' showing total 24 results
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24 results on '"Kha, Quang-Hien"'

1. Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis

Author

Nguyen, Thanh-Huy, Kha, Quang Hien, Truong, Thai Ngoc Toan, Lam, Ba Thinh, Ngo, Ba Hung, Dinh, Quang Vinh, and Le, Nguyen Quoc Khanh

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. In the first problem, many multi-view methods have been released for concatenating features of two or more views for the training and inference stage. Having said that, most multi-view existing methods are not explainable in the meaning of feature fusion, and treat many views equally for diagnosing. Our work aims to propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view (ipsilateral view) before learning the high-level feature that contains the cancerous features. For the second issue, we also propose a simple but novel malignant mammogram synthesis framework for upsampling minor class samples. Our easy-to-implement and no-training framework has eliminated the current limitation of the CutMix algorithm which is unreliable synthesized images with random pasted patches, hard-contour problems, and domain shift problems. Our results on VinDr-Mammo and CMMD datasets show the effectiveness of our two new frameworks for both multi-view training and synthesizing mammographic images, outperforming the previous conventional methods in our experimental settings.

Published
2023

2. RF-Lung-DR: Integrating Biological and Drug SMILES Features in a Random Forest-Based Drug Response Predictor for Lung Cancer Cell Lines

Author

Tran, Thi-Oanh, Kha, Quang-Hien, Le, Nguyen Quoc Khanh, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chen, Hao, editor, Zhou, Yuyin, editor, Xu, Daguang, editor, and Vardhanabhuti, Varut Vince, editor

Published
2024
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14. Two-stage revision for treatment of tuberculous prosthetic hip infection: an outcome analysis

Author

Tuan Van Le, Tran Binh Duong, Kha Quang Hien, Quyen Nguyen Quang Ton, Tan Huyn, Tran Phuoc Binh, Dao Thanh Tu, Pham Phuoc Tho, Le Nguyen Binh, Huynh Phuoc Hau, and Truong Nguyen Khanh Hung

Subjects
Orthopedics and Sports Medicine, Surgery
Abstract

Prosthetic joint infections (PJI) and especially tuberculosis (TB) PJI are rare diseases and hard to cure. The effectiveness of treatments for tuberculous PJI still remains a problem. The objective of this research was to indicate the success of two-stage revision replacement and also giving the associated criteria.From 2015 to 2020, five patients with tuberculous PJI were treated with two-stage revision at Cho Ray hospital, Vietnam. We collected the dataset which included demographic data, the interval from the time of joint replacement to reported infection, records of tuberculous PJI, administration of anti-TB medications (duration, months), history of operation(s), duration of follow-up, and specific type(s) of antibiotics loaded in bone cement. The approval for this study was made by the institutional review board from Cho Ray Hospital, Vietnam. We conducted a literature review based on the keywords "PJI" and "TB" on PubMed.Five patients [median age 66 years (range 35-84)] had found tuberculous PJI. The median time from arthroplasty to diagnosis was 19 months (range 4-48). The diagnosis was confirmed by joint aspirates or synovial tissue. Positive PCR was also reported in all cases. The average duration of anti-tuberculosis polytherapy administration was 14.4 months. The operative techniques on five patients included debridement and using spacer loaded with 2 g streptomycin (and 2 g vancomycin if they got a coinfection) for 1 pack of bone cement, and revision arthroplasty. In most cases, the outcome of treatment using two-stage revision replacement was 80%. Overall, the auxiliary bacterial infections were recognized in three patients with tuberculous PJI and Staphylococcus aureus. Streptomycin and vancomycin were loaded in a cement spacer to increase the success rate, and tuberculous PJI was controlled for all patients.Tuberculous PJI can be controlled with two-stage revision replacement with an antibiotic-loaded cement spacer that is molded intraoperatively with custom mold and prolonged anti-tuberculosis treatment in all cases.IV.

Published
2022

15. An interpretable deep learning model for classifying adaptor protein complexes from sequence information

Author

Kha, Quang Hien, Tran, Thi Oanh, Nguyen, Trinh Trung Duong, Nguyen, Van Nui, Than, Khoat, Le, Nguyen Quoc Khanh, Kha, Quang Hien, Tran, Thi Oanh, Nguyen, Trinh Trung Duong, Nguyen, Van Nui, Than, Khoat, and Le, Nguyen Quoc Khanh

Abstract

Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex techniques, which were then advanced by machine learning and computational approaches to facilitate the APs recognition task. However, most studies focused on recognizing separate ones in the APs family or the APs in general with non-APs, lacking one comprehensive strategy to distinguish the complexes of AP subtypes. Herein, we proposed a novel method to implement one novel task as discriminating the AP complexes in the APs family, utilizing an interpretable deep neural network architecture on sequence-based encoding features. This work also introduced a benchmark data set of AP complexes originating from the UniProt and GeneOntology databases. To assess the robustness of our proposed method, we compared our performance to various machine learning algorithms and feature extraction strategies. Furthermore, the interpretation of the model's prediction performance was implemented using t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and SHapley Additive exPlanations (SHAP) analysis to show the distribution of AP complexes on optimal features. The promising performance of our architecture can assist scientists not only in AP complexes distinction but also in general protein sequences. Moreover, we have also made our work publicly on GitHub https://github.com/khanhlee/adaptor-dnn.

Published
2022

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