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End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

2020-12-02 · Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan, Rong Zhang, Avi Sil, Vittorio Castelli, Radu Florian, Salim Roukos

End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages. Recent work has successfully trained neural IR systems using only supervised question answering (QA) examples from open-domain datasets. However, despite impressive performance on Wikipedia, neural IR lags behind traditional term matching approaches such as BM25 in more specific and specialized target domains such as COVID-19. Furthermore, given little or no labeled data, effective adaptation of QA systems can also be challenging in such target domains. In this work, we explore the application of synthetically generated QA examples to improve performance on closed-domain retrieval and MRC. We combine our neural IR and MRC systems and show significant improvements in end-to-end QA on the CORD-19 collection over a state-of-the-art open-domain QA baseline.

📄 PDF Abstract BibTeX arXiv:2012.01414

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Tasks

Domain Adaptation Information Retrieval Machine Reading Comprehension Question Answering Reading Comprehension Retrieval

Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis

2021-06-17 · Hasib Zunair, A. Ben Hamza

We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models. The dataset consists of 21,295 synthetic COVID-19 chest X-ray images to be used for computer-aided diagnosis.…

Diagnostic Domain Adaptation Management Unsupervised Domain Adaptation

Unsupervised domain adaptation based COVID-19 CT infection segmentation network

2020-11-23 · Han Chen, Yifan Jiang, Murray Loew, Hanseok Ko

Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnosis approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accu…

Computed Tomography (CT)Domain Adaptation Segmentation Unsupervised Domain Adaptation

Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation

2020-10-20 · Hasib Zunair, A. Ben Hamza

Motivated by the lack of publicly available datasets of chest radiographs of positive patients with Coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray image…

COVID-19 Diagnosis Domain Adaptation image-classification Image Classification+5

DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs

2020-08-26 · Pengyi Zhang, Yunxin Zhong, Yulin Deng, Xiaoying Tang 외

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination. We propose a novel approach, called DRR4Covid, to learn automated COVID-19 diagnosis and infe…

COVID-19 Diagnosis Domain Adaptation Segmentation

Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup

2022-10-06 · Huimin Zeng, Zhenrui Yue, Ziyi Kou, Lanyu Shang 외

In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of t…

Contrastive Learning Domain Adaptation Misinformation Unsupervised Domain Adaptation