Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis

• Published in: IEEE journal of biomedical and health informatics 2023-01, Vol.27 (1), p.457-468
• Main Authors: Caughlin, Kayla, Duran-Sierra, Elvis, Cheng, Shuna, Cuenca, Rodrigo, Ahmed, Beena, Ji, Jim, Martinez, Mathias, Al-Khalil, Moustafa, Al-Enazi, Hussain, Cheng, Yi-Shing Lisa, Wright, John, Jo, Javier A., Busso, Carlos
• Format: Article
• Language: English
• Subjects: Adaptation models; Algorithms; Automated oral cancer diagnosis; Calibration; Cancer; Classifiers; Data centers; Data collection; Data models; Datasets; Deep learning; Diagnostic Imaging; Diagnostic systems; domain adaptation; Feature extraction; gradient reversal; Humans; Image analysis; Image processing; Imaging; Labelling; Machine learning; Medical diagnosis; Medical imaging; Mouth Neoplasms; multispectral autofluorescence imaging; Neural networks; Neural Networks, Computer; Oral cancer; Systems development; Task analysis; variance regularization
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2022.3217015

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains ( p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.