Image Segmentation as an Instrument for Setting Attention Regions in Convolutional Neural Networks for Bias Detection Purposes

Bojana Velichkovska, Danijela Efnusheva, Marija Kalendar and Goran Jakimovski

References:

1. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection Region Proposal Networks,” Advances in Neural Information Processing Systems, vol. 28, 2015.
2. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
3. P. Dutta, P. Upadhyay, M. De, and R. G. Khalkar, “Medical Image Analysis using Deep Convolutional Neural Networks: CNN Architectures and Transfer Learning,” 2020 IC on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020.
4. P. Bir, and V. E. Balas, “A Review on Medical Image Analysis with Convolutional Neural Networks,” 2020 IEEE IC on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, 2020.
5. P. Kalyani, S. Srivastava, A. Reddyprasad, R. Krishnamoorthy, S. Arun, and S. Padmapriya, “Medical Image Processing from Large Datasets Using Deep Learning,” 2021 3rd IC on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2021.
6. J. E. A. Ovalle, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. Á. Guevara-López, “Representation learning for mammography mass lesion classification with convolutional neural networks”, Computer Methods and Programs in Biomedicine, vol. 127, pp. 248-257, 2016.
7. B. Shetty, R. Fernandes, A.P. Rodrigues, R. Chengoden, S. Bhattacharya, and K. Lakshmanna, “Skin lesion classification of dermoscopic images using machine learning and convolutional neural network,” Sci Rep, vol. 12, no. 1, pp. 18134, 2022.
8. S. P. Jillella, C. Rohith, S. Shameem and P. S. S. Babu, “ECG Classification For Arrhythmias using CNN & Heart Disease Prediction using Web application,” 2022 First IC on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichy, India, 2022.
9. O. Oktay, E. Ferrante, K. Kamnitsas, M. Heinrich, W. Bai, J. Caballero, et al., “Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation,” IEEE Transactions on Medical Imaging, vol. 37, no. 2, pp. 384-395, 2018.
10. S. Romano, D. Fucci, G. Scanniello, M. Teresa Baldassarre, B. Turhan, and N. Juristo, “Researcher Bias in Software Engineering Experiments: a Qualitative Investigation,” 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Portoroz, Slovenia, 2020.
11. J. A. Sabin, “Tackling Implicit Bias in Health Care”, New England Journal of Medicine, vol. 387, no. 2, pp. 105-107, 2022.
12. J. W. Gichoya, I. Banerjee, A. R. Bhimireddy, and et al., “AI recognition of patient race in medical imaging: a modelling study,” in The Lancet Digital Health, vol. 4, no. 6, pp. 406-414, 2022.
13. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Medical Image Computing and Computer-Assisted Intervention, Springer, vol. 9351, pp. 234-241, 2015.
14. S. Jaeger, S. Candemir, S. Antani, Y. X. Wáng, P. X. Lu, and G. Thoma, “Two public chest X-ray datasets for computer-aided screening of pulmonary diseases,” Quant Imaging Med Surg, vol. 4, no. 6, pp. 475-477, 2014.
15. E. Colak, F. C. Kitamura, S. B. Hobbs, and et al., “The RSNA Pulmonary Embolism CT Dataset,” Radiology: Artificial Intelligence, vol. 3, no. 2, p. e200254, 2021.
16. M. M. Jawaid, R. Rajani, P. Liatsis, C. C. Reyes-Aldasoro, and G. Slabaugh, “Improved CTA Coronary Segmentation with a Volume-Specific Intensity Threshold,” Medical Image Understanding and Analysis, pp. 207-218, 2017.
17. D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization”, 2014.