Please use this identifier to cite or link to this item:
Title: Study on Data Partition for Delimitation of Masses in Mammography
Authors: Viegas, Luís 
Domingues, Inês
Mendes, Mateus
Keywords: Breast mass; Computer-aided detection; Dataset partition; Mammography;; Mask R-CNN; Mass detection; Mass segmentation
Issue Date: 2021
Project: UIDB/00048/2020 
Serial title, monograph or event: Journal of Imaging
Volume: 7
Issue: 9
Abstract: Mammography is the primary medical imaging method used for routine screening and early detection of breast cancer in women. However, the process of manually inspecting, detecting, and delimiting the tumoral massess in 2D images is a very time-consuming task, subject to human errors due to fatigue. Therefore, integrated computer-aided detection systems have been proposed, based on modern computer vision and machine learning methods. In the present work, mammogram images from the publicly available Inbreast dataset are first converted to pseudo-color and then used to train and test a Mask R-CNN deep neural network. The most common approach is to start with a dataset and split the images into train and test set randomly. However, since there are often two or more images of the same case in the dataset, the way the dataset is split may have an impact on the results. Our experiments show that random partition of the data can produce unreliable training, so the dataset must be split using case-wise partition for more stable results. In experimental results, the method achieves an average true positive rate of 0.936 with 0.063 standard deviation using random partition and 0.908 with 0.002 standard deviation using case-wise partition, showing that case-wise partition must be used for more reliable results. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
ISSN: 2313-433X
DOI: 10.3390/jimaging7090174
Rights: openAccess
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat
jimaging-07-00174.pdf569.12 kBAdobe PDFView/Open
Show full item record


checked on Aug 2, 2022

Page view(s)

checked on Aug 12, 2022


checked on Aug 12, 2022

Google ScholarTM




This item is licensed under a Creative Commons License Creative Commons