Yash Khare*, Viraj Bagal*, Minesh Mathew, Adithi Devi, U Deva Priyakumar, CV Jawahar

Abstract: Images in the medical domain are fundamentally different from the general domain images. Consequently, it is infeasible to directly employ general domain Visual Question Answering ( VQA ) models for the medical domain. Additionally,medical images annotation is a costly and time-consuming process. To overcome these limitations, we propose a solution inspired by self-supervised pretraining of Transformer-style architectures for NLP , V ision and L anguage tasks. Our method involves learning richer medical image and text semantic representations using Masked Language Modeling (MLM) with image features as the pretext task on a large medical image+caption dataset. The proposed solution achieves new state-of-the-art performance on two VQA datasets for radiology images – VQA - M ed 2019 and VQA - RAD , outperforming even the ensemble models of previous best solutions. Moreover, our solution provides attention maps which help in model interpretability.

python train_vqarad.py --run_name give_name --mixed_precision --use_pretrained --lr set_lr  --epochs set_epochs

python train.py --run_name  give_name --mixed_precision --lr set_lr --category cat_name --batch_size 16 --num_vis set_visual_feats --hidden_size hidden_dim_size

python eval.py --run_name give_name --mixed_precision --category cat_name --hidden_size hidden_dim_size --use_pretrained

MMBERT General, which is a single model for both the question types in the dataset, outperforms the existing approaches including the ones which have a dedicated model for each question type.

Our MMBERT Exclusive achieves state-of-the-art results on the overall accuracy and BLEU score, even surpassing CGMVQA E ns. which is an ensemble of 3 dedicated models for each category. Even our MMBERT General performs better than the CGMVQA Ens. on the abnormality and yes/no categories. Additionally, our MMBERT General outperforms single dedicated CGMVQA models in all the categories but modality.