Academic Journal
Breast cancer classification in point-of-care ultrasound imaging—the impact of training data
| Τίτλος: | Breast cancer classification in point-of-care ultrasound imaging—the impact of training data |
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| Συγγραφείς: | Karlsson, Jennie, Arvidsson, Ida, Sahlin, Freja, Åström, Kalle, Overgaard, Niels Christian, Lång, Kristina, Heyden, Anders |
| Συνεισφορές: | Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematics (Faculty of Engineering), Computer Vision and Machine Learning, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematik LTH, Datorseende och maskininlärning, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Proactive Ageing, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Proaktivt åldrande, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: AI and Digitalization, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: AI och digitalisering, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Nature-based future solutions, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturbaserade framtidslösningar, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Light and Materials, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Ljus och material, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Engineering Health, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Teknik för hälsa, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section V, Diagnostic Radiology, (Lund), Stroke Imaging Research group, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion V, Diagnostisk radiologi, Lund, Stroke Imaging Research group, Originator, Lund University, Faculty of Science, Centre for Mathematical Sciences, Research groups at the Centre for Mathematical Sciences, Mathematical Imaging Group, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Forskargrupper vid Matematikcentrum, Mathematical Imaging Group, Originator, Lund University, Faculty of Science, Centre for Mathematical Sciences, Research groups at the Centre for Mathematical Sciences, Partial differential equations, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Forskargrupper vid Matematikcentrum, Partiella differentialekvationer, Originator, Lund University, Profile areas and other strong research environments, Other Strong Research Environments, LUCC: Lund University Cancer Centre, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Övriga starka forskningsmiljöer, LUCC: Lunds universitets cancercentrum, Originator, Lund University, Faculty of Medicine, Department of Translational Medicine, Radiology Diagnostics, Malmö, Lunds universitet, Medicinska fakulteten, Institutionen för translationell medicin, Diagnostisk radiologi, Malmö, Originator |
| Πηγή: | Journal of Medical Imaging. 12(1):1-16 |
| Θεματικοί όροι: | Natural Sciences, Computer and Information Sciences, Computer graphics and computer vision, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datorgrafik och datorseende, Engineering and Technology, Medical Engineering, Medical Imaging, Teknik, Medicinteknik, Medicinsk bildvetenskap |
| Περιγραφή: | Purpose: The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniques for increasing the amount of training data. Approach: Two data sets consisting of breast tissue images were collected, one captured with POCUS and another with standard ultrasound (US). The data sets were expanded by using different techniques, including augmentation, histogram matching, histogram equalization, and cycle-consistent adversarial networks (CycleGANs). A classification network was trained on different combinations of the original and expanded data sets. Different types of augmentation were investigated and two different CycleGAN approaches were implemented. Results: Almost all methods for expanding the data sets significantly improved the classification results compared with solely using POCUS images during the training of the classification network. When training the classification network on POCUS and CycleGAN-generated POCUS images, it was possible to achieve an area under the receiver operating characteristic curve of 95.3% (95% confidence interval 93.4% to 97.0%). Conclusions: Applying augmentation during training showed to be important and increased the performance of the classification network. Adding more data also increased the performance, but using standard US images or CycleGAN-generated POCUS images gave similar results. |
| Σύνδεσμος πρόσβασης: | https://doi.org/10.1117/1.JMI.12.1.014502 |
| Βάση Δεδομένων: | SwePub |
| ISSN: | 23294302 23294310 |
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| DOI: | 10.1117/1.JMI.12.1.014502 |