id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-140679-r6exuzxs	Calderon-Ramirez, Saul	Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images	2020-08-19		.txt	text/plain	7217	448	56	In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch using a very limited number of labelled observations and highly imbalanced labelled dataset. This research extends a novel Semi-supervised Deep Learning (SSDL) framework known as MixMatch [10] for the detection of COVID-19 based on chest X-ray images. As typical deep learning architectures require many labelled images, we aim to explore the usage of SSDL for COVID-19 detection using X-ray images, evaluating it under another frequent challenge; data imbalance. We aim to assess MixMatch's performance under real-world scenarios, specifically medical imaging in the context of a virus outbreak, where small labelled samples are available with a strong under-representation of the new pathology, leading to imbalanced datasets. We also make available a first sample of a chest-X ray dataset from the Costa Rican medical private clinic Imagenes Medicas Dr. Chavarria Estrada, with observations containing no findings, and test its usage for training the SSDL framework.	./cache/cord-140679-r6exuzxs.txt	./txt/cord-140679-r6exuzxs.txt