Visual Question Answering System for Identifying Medical Images Attributes

2020 ◽  
pp. 483-492
Author(s):  
Afrae Bghiel ◽  
Yousra Dahdouh ◽  
Imane Allaouzi ◽  
Mohamed Ben Ahmed ◽  
Abdelhakim Anouar Boudhir
Keyword(s):  
Question Answering ◽  
Medical Images ◽  
Question Answering System ◽  
Visual Question Answering

A new visual question answering system for medical images characterization

2019 ◽  
Author(s):  
Bghiel Afrae ◽  
Dahdouh Yousra ◽  
Allaouzi Imane ◽  
Ben Ahmed Mohamed ◽  
Anouar Boudhir Abdelhakim
Keyword(s):  
Question Answering ◽  
Medical Images ◽  
Question Answering System ◽  
Visual Question Answering

scholarly journals Predicting drug–protein interaction using quasi-visual question answering system

2020 ◽  
Vol 2 (2) ◽  
pp. 134-140 ◽  
Author(s):  
Shuangjia Zheng ◽  
Yongjian Li ◽  
Sheng Chen ◽  
Jun Xu ◽  
Yuedong Yang
Keyword(s):  
Protein Interaction ◽  
Question Answering ◽  
Question Answering System ◽  
Visual Question Answering

scholarly journals MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dhruv Sharma ◽  
Sanjay Purushotham ◽  
Chandan K. Reddy
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Medical Images ◽  
Learning Model ◽  
Training Data ◽  
Human Errors ◽  
Medical Practitioners ◽  
Visual Question Answering ◽  
Deep Learning Model ◽  
Real World Problems

AbstractMedical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision maker. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a ‘second opinion’ on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction—categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model’s predicted results.

scholarly journals Publisher Correction: Predicting drug–protein interaction using quasi-visual question answering system

2020 ◽  
Vol 2 (9) ◽  
pp. 551-551
Author(s):  
Shuangjia Zheng ◽  
Yongjian Li ◽  
Sheng Chen ◽  
Jun Xu ◽  
Yuedong Yang
Keyword(s):  
Protein Interaction ◽  
Question Answering ◽  
Question Answering System ◽  
Visual Question Answering

Visual Question-Answering System Using Integrated Models of Image Captioning and BERT

2021 ◽  
pp. 169-190
Author(s):  
Lavika Goel ◽  
Mohit Dhawan ◽  
Rachit Rathore ◽  
Satyansh Rai ◽  
Aaryan Kapoor ◽  
...  
Keyword(s):  
Question Answering ◽  
Integrated Models ◽  
Image Captioning ◽  
Question Answering System ◽  
Visual Question Answering
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