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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes.<\/jats:p>","DOI":"10.1007\/s10278-024-01018-9","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T20:01:47Z","timestamp":1710446507000},"page":"1767-1782","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8327-1169","authenticated-orcid":false,"given":"Rodrigo","family":"Escobar D\u00edaz Guerrero","sequence":"first","affiliation":[]},{"given":"Lina","family":"Carvalho","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Bocklitz","sequence":"additional","affiliation":[]},{"given":"Juergen","family":"Popp","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 Luis","family":"Oliveira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"key":"1018_CR1","doi-asserted-by":"publisher","unstructured":"Perez L, Wang J: The Effectiveness of Data Augmentation in Image Classification using Deep Learning, arxiv, https:\/\/doi.org\/10.48550\/arXiv.1712.04621, 2017.","DOI":"10.48550\/arXiv.1712.04621"},{"issue":"1","key":"1018_CR2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM: A survey on Image Data Augmentation for Deep Learning, J Big Data,6(1):60. https:\/\/doi.org\/10.1186\/s40537-019-0197-0, 2019.","journal-title":"J Big Data"},{"issue":"07","key":"1018_CR3","doi-asserted-by":"publisher","first-page":"13001","DOI":"10.1609\/aaai.v34i07.7000","volume":"34","author":"Z Zhong","year":"2020","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y: Random Erasing Data Augmentation, Proc AAAI Conf Artif Intell,34(07):13001-13008.\u00a0https:\/\/doi.org\/10.1609\/aaai.v34i07.7000, 2020.","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"6","key":"1018_CR4","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1007\/s10845-021-01738-7","volume":"32","author":"S Meister","year":"2021","unstructured":"Meister S, M\u00f6ller N, St\u00fcve J, Groves RM: Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set, J Intell Manuf, 32(6):1767-1789. https:\/\/doi.org\/10.1007\/s10845-021-01738-7, 2021.","journal-title":"J Intell Manuf"},{"issue":"1","key":"1018_CR5","doi-asserted-by":"publisher","first-page":"43","DOI":"10.4103\/jpi.jpi_103_20","volume":"12","author":"L Jose","year":"2021","unstructured":"Jose L, Liu S, Russo C, Nadort A, Di Ieva A: Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review, J Pathol Inform, 12(1):43. https:\/\/doi.org\/10.4103\/jpi.jpi_103_20, 2021.","journal-title":"J Pathol Inform"},{"issue":"11","key":"1018_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"IJ Goodfellow","year":"2020","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, et al: Generative Adversarial Networks. 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