Autism spectrum disorder, is a developmental disorder characterized by persistent problems in social communication and i nteraction, along with restricted and repetitive patterns of behavior, interests or activities Research has revealed that brain connectivity analysis provides crucial insights to pinpoint the differences between autistic and typically developing (TD) children during development. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. In our approach we are exploring different static and dynamic functional connectivity properties which can be extracted using resting state fmri scans and can well differentiate these autistic children from healthy children accurately.​

Visual Question Answering (VQA) on medical images aspires to build models that can answer diagnostically relevant questions asked on medical images and can provide valuable additional insights to medical professionals. For this task we propose a solution inspired by self-supervised pretraining of Transformer-style architectures for NLP, Vision, and Language tasks. Our method involves learning richer medical image and text semantic representations using Masked Vision-Language Modeling as the pretext task on a large medical image+caption dataset.​