@MASTERSTHESIS{ 2021:1463717524,
 	title = {Pathology localization on chest radiographs with limited supervision via semi-supervised multiple instance learning},
 	year = {2021},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/9790",
 	abstract = "Radiographs are the primary examination for diagnosing chest conditions, and yet they
 are frequently misread/misdiagnosed due to human-observer confusion. In clinical practice,
 there is an increase of deep learning approaches to support radiologists on the decision-making
 process to improve diagnostic accuracy. To properly support radiologists, it is insufficient for the
 system to simply output a diagnosis label. Ideally, the model should provide more information
 to support the classification result, such as the spatial localization of the finding. To properly
 train deep learning models, we usually need lots of annotated data. There is a vast amount
 of publicly-available chest radiographs labeled according to their radiological findings (labels
 for classification), but very few contain a location annotation. Our goal is to extend the use
 of unlabeled data to improve pathology localization in chest radiographs in a scenario with
 limited labeled data. We identify state-of-the-art semi-supervised methods and evaluated their
 performance on a classification scenario. Next, we extend the best method, Mean Teacher, to
 perform localization within a multiple instance learning framework, introducing our method
 C-MIL. Multiple instance learning is a paradigm with two types of labels: a general label
 that is known, and a more specific and unknown label but related to the one known, in our
 case, pathology presence and its localization. Our results show improvements of applying
 consistency regularization over a multiple instance localization framework and demonstrate that
 semi-supervised learning methods are promising to advance the state-of-the-art performance of
 pathology localization methods.",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Escola Polit?cnica}
}