Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine (2403.03642v1)
Abstract: Recently, with increasing interest in pet healthcare, the demand for computer-aided diagnosis (CAD) systems in veterinary medicine has increased. The development of veterinary CAD has stagnated due to a lack of sufficient radiology data. To overcome the challenge, we propose a generative active learning framework based on a variational autoencoder. This approach aims to alleviate the scarcity of reliable data for CAD systems in veterinary medicine. This study utilizes datasets comprising cardiomegaly radiograph data. After removing annotations and standardizing images, we employed a framework for data augmentation, which consists of a data generation phase and a query phase for filtering the generated data. The experimental results revealed that as the data generated through this framework was added to the training data of the generative model, the frechet inception distance consistently decreased from 84.14 to 50.75 on the radiograph. Subsequently, when the generated data were incorporated into the training of the classification model, the false positive of the confusion matrix also improved from 0.16 to 0.66 on the radiograph. The proposed framework has the potential to address the challenges of data scarcity in medical CAD, contributing to its advancement.
- X. Zhu, C. Vondrick, C. C. Fowlkes, and D. Ramanan, “Do we need more training data????,” International Journal of Computer Vision (IJCV), vol. 119, no. 1, pp. 76–92, 2016.
- V. Gudivada, A. Apon, and J. Ding, “Data quality considerations for big data and machine learning: going beyond data cleaning and transformations,” International Journal on Advances in Software, vol. 10, no. 1, pp. 1–20, 2017.
- J.-Y. Oh, I.-G. Lee, H.-H. Chang, E. Lee, and J.-H. Jeong, “Application of a dual-stage deep learning framework to detect left atrial enlargement for pet heart failure,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), Hawaii, USA, Oct. 1-4, 2023.
- D. Yoon, H.-J. Kong, B. S. Kim, W. S. Cho, J. C. Lee, M. Cho, M. H. Lim, S. Y. Yang, S. H. Lim, and J. Lee, “Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network,” Scientific Reports, vol. 12, no. 1, p. 261, 2022.
- M. La Salvia, E. Torti, R. Leon, H. Fabelo, S. Ortega, B. Martinez-Vega, G. M. Callico, and F. Leporati, “Deep convolutinal generative adversarial networks to enhance artificial intelligence in healthcare: a skin cancer application,” Sensors, vol. 22, no. 16, p. 6145, 2022.
- J.-J. Zhu and J. Bento, “Generative adversarial active learning,” arXiv preprint arXiv:1702.07956, 2017.
- D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013.
- C. Lam, B. J. Gavaghan, and F. E. Meyers, “Radiographic quantification of left atrial size in dogs with myxomatous mitral valve disease,” Journal of Veterinary Internal Medicine (JVIM), vol. 35, no. 2, pp. 747–754, 2021.
- A. Sauer, K. Chitta, J. Müller, and A. Geiger, “Projected GANs converge faster,” Advances in Neural Information Processing Systems (NIPS), vol. 34, pp. 17 480–17 492, 2021.
- M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in Neural Information Processing Systems (NIPS), vol. 30, 2017.
- F. Rahutomo, T. Kitasuka, and M. Aritsugi, “Semantic cosine similarity,” in International Student Conference on Advanced Science and Technology (ICAST), Seoul, South Korea, Oct. 29-30, vol. 4, no. 1, 2012, p. 1.
- A. K. Chaudhary, S. Roy, R. Rizk, and K. Santosh, “Automated fracture detection from CT scans,” in IEEE International Conference on Artificial Intelligence (CAI), 2023, pp. 161–162.
- J. Estrada, Y. Zhigang, S. Datta, N. Duraisamy, J. De Guia, O. Cheng Hun, G. Opina, and A. Tripathi, “AV in action: a development of robust and efficient planning and perception system for autonomous food delivery vehicle,” in IEEE International Conference on Artificial Intelligence (CAI), 2023, pp. 19–20.
- D. M. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” arXiv preprint arXiv:2010.16061, 2020.
- A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, pp. 168–192, 2020.
- J.-H. Jeong, J.-H. Cho, B.-H. Lee, and S.-W. Lee, “Real-time deep neurolinguistic learning enhances noninvasive neural language decoding for brain–machine interaction,” IEEE Transactions on Cybernetics, vol. 53, no. 12, pp. 7469–7482, 2023.
- J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems (NIPS), vol. 33, pp. 6840–6851, 2020.
- Song, Jiaming and Meng, Chenlin and Ermon, Stefano, “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502, 2020.