MoBVQA: A Modality based Medical Image Visual Question Answering System
Arshiya Lubna, Saidalavi Kalady, A Lijiya · 2019
This paper discusses about the work on medical image visual question answering done on the ImageCLEF 2019 medical VQA dataset. Visual question answering is a task where an image and a related question is given as input to the machine and we get a correct answer to the question as output. In our problem, both the input image and question are from medical domain. In medical imaging, VQA has applications like providing a second opinion to radiologists about their analysis of the image. It can also be used by the patients for getting a basic information about the image without consulting the doctor. We have considered the problem of answering modality based questions for medical images like X-ray, Computed Tomography(CT), ultra sound(US), magnetic resonance imaging(MRI) etc. The approach used here is to use a Convolutional Neural Network(CNN) to classify the input image to its modality class and thus generate the answer according to the CNN output. The proposed model shows a testing accuracy of 83.8% which is comparable with state of the art.