2019-05-24-gupta19b.md

section	title	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Contributed Papers	Generative Image Translation for Data Augmentation of Bone Lesion Pathology	Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases. In this work, we address the problem of classifying bone lesions from X-ray images by increasing the small number of positive samples in the training set. We propose a generative data augmentation approach based on a cycle-consistent generative adversarial network that synthesizes bone lesions on images without pathology. We pose the generative task as an image-patch translation problem that we optimize specifically for distinct bones (humerus, tibia, femur). In experimental results, we confirm that the described method mitigates the class imbalance problem in the binary classification task of bone lesion detection. We show that the augmented training sets enable the training of superior classifiers achieving better performance on a held-out test set. Additionally, we demonstrate the feasibility of transfer learning and apply a generative model that was trained on one body part to another.	inproceedings	Proceedings of Machine Learning Research	gupta19b	0	Generative Image Translation for Data Augmentation of Bone Lesion Pathology	225	235	225-235	225	false	Gupta, Anant and Venkatesh, Srivas and Chopra, Sumit and Ledig, Christian	given family Anant Gupta given family Srivas Venkatesh given family Sumit Chopra given family Christian Ledig	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/gupta19b/gupta19b.pdf