Abstract
The factors that concern the current AI medical models are the lack of generalizing capability when they are subjected to clinical data and also the scarcity of labeled medical data from which they can learn. This paper studies the role of transfer learning by fine-tuning the network when different fractions of medical data are available at the downstream task of diabetic retinopathy (DR) severity detection. The experimental results signify that supervised pre-training on ImageNet, followed by fine-tuning on labeled domain-specific fundus images significantly improves the efficacy of the medical image classifier when trained on full training data thereby suggesting transfer learning works. But what is less known is how the fine-tuning performance is affected when subjected to different fractions of data and if the learning is label efficient. Hence, we investigate the performance of the model under different fractions of labeled data (20 %, 40 %, 60 %, and 80 % of the entire data) on DR classification task, the results suggest that supervised fine-tuning underperforms when model is trained under low data regime. The proposed model achieves test accuracy of 0.8010, AUC of 0.86, F1 score of 0.6477, and cohen kappa score of 0.7007 when trained on full training data but underperforms when subjected to low data regime. Thereby suggesting the limits of supervised learning when the model is trained using limited annotated data. Hence our work opens door to further research in achieving good performance at low data regimes.
Similar content being viewed by others
Data Availability
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
References
Adak, C., Karkera, T., Chattopadhyay, S., Saqib, M.: Detecting severity of diabetic retinopathy from fundus images using ensembled transformers. arXiv preprint http://arxiv.org/abs/2301.00973arXiv:2301.00973 (2023)
Alyoubi WL, Shalash WM, Abulkhair MF (2020) Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked 20:100377
Alzubaidi, L., Fadhel, M.A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., R Oleiwi, S.: Towards a better understanding of transfer learning for medical imaging: a case study. Applied Sciences 10(13), 4523 (2020)
Alzubaidi L, Al-Amidie M, Al-Asadi A, Humaidi AJ, Al-Shamma O, Fadhel MA, Zhang J, Santamaría J, Duan Y (2021) Novel transfer learning approach for medical imaging with limited labeled data. Cancers 13(7):1590
Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., : Big self-supervised models advance medical image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3478–3488 (2021)
Bianco S, Cadene R, Celona L, Napoletano P (2018) Benchmark analysis of representative deep neural network architectures. IEEE access 6:64270–64277
Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA (2020) Albumentations: fast and flexible image augmentations. Information 11(2):125
Castro DC, Walker I, Glocker B (2020) Causality matters in medical imaging. Nature Communications 11(1):1–10
Cavan D, Makaroff L, da Rocha Fernandes J, Sylvanowicz M, Ackland P, Conlon J, Chaney D, Malhi A, Barratt J (2017) The diabetic retinopathy barometer study: global perspectives on access to and experiences of diabetic retinopathy screening and treatment. Diabetes research and clinical practice 129:16–24
Chaudhary, A.: The Illustrated Self-Supervised Learning. https://amitness.com/2020/02/illustrated-self-supervised-learning (2020)
Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. Advances in neural information processing systems 30 (2017)
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Gabruseva, T., Poplavskiy, D., Kalinin, A.: Deep learning for automatic pneumonia detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 350–351 (2020)
Gifani P, Shalbaf A, Vafaeezadeh M (2021) Automated detection of covid-19 using ensemble of transfer learning with deep convolutional neural network based on ct scans. International journal of computer assisted radiology and surgery 16:115–123
Graziani, M., Andrearczyk, V., Müller, H.: Visualizing and interpreting feature reuse of pretrained cnns for histopathology. In: Irish Machine Vision and Image Processing Conference (IMVIP 2019), Dublin, Ireland (2019)
Gu J, Sun X, Zhang Y, Fu K, Wang L (2019) Deep residual squeeze and excitation network for remote sensing image super-resolution. Remote Sensing 11(15):1817
Hagos, M.T., Kant, S.: Transfer learning based detection of diabetic retinopathy from small dataset. arXiv preprint http://arxiv.org/abs/1905.07203arXiv:1905.07203 (2019)
Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: Review of methods and applications. Expert systems with applications 73:220–239
Harangi, B., Toth, J., Baran, A., Hajdu, A.: Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2699–2702 (2019). IEEE
Hart, B., Achakulvisut, T., Adeyemi, A., Akrami, A., Alicea, B., Alonso-Andres, A., Alzate-Correa, D., Ash, A., Ballesteros, J., Balwani, A., et al.: Neuromatch academy: a 3-week, online summer school in computational neuroscience. Journal of Open Source Education 5(49) (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint http://arxiv.org/abs/1704.04861arXiv:1704.04861 (2017)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Huang S, Li J, Xiao Y, Shen N, Xu T (2022) Rtnet: relation transformer network for diabetic retinopathy multi-lesion segmentation. IEEE Transactions on Medical Imaging 41(6):1596–1607
Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. Journal of Medical Imaging 3(3):034501–034501
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015). pmlr
Jampol, L.M., Tadayoni, R., Ip, M.: Need for a new classification of diabetic retinopathy. Retina (Philadelphia, Pa.) 41(3), 459 (2021)
Jampol LM, Glassman AR, Sun J (2020) Evaluation and care of patients with diabetic retinopathy. New England Journal of Medicine 382(17):1629–1637
Jiwani, N., Gupta, K., Afreen, N.: A convolutional neural network approach for diabetic retinopathy classification. In: 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), pp. 357–361 (2022). 10.1109/CSNT54456.2022.9787577
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6):84–90
Lam C, Yi D, Guo M, Lindsey T (2018) Automated detection of diabetic retinopathy using deep learning. AMIA summits on translational science proceedings 2018:147
Li F, Wang Y, Xu T, Dong L, Yan L, Jiang M, Zhang X, Jiang H, Wu Z, Zou H (2022) Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye 36(7):1433–1441
Lin J, Cai Q, Lin M (2021) Multi-label classification of fundus images with graph convolutional network and self-supervised learning. IEEE Signal Processing Letters 28:454–458
Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, Kanada K, de Oliveira Marinho G, Gallegos J, Gabriele S (2020) A deep learning system for differential diagnosis of skin diseases. Nature medicine 26(6):900–908
Lones, M.A.: How to avoid machine learning pitfalls: a guide for academic researchers. arXiv preprint http://arxiv.org/abs/2108.02497arXiv:2108.02497 (2021)
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A (2020) International evaluation of an ai system for breast cancer screening. Nature 577(7788):89–94
Ohri K, Kumar M (2021) Review on self-supervised image recognition using deep neural networks. Knowledge-Based Systems 224:107090
Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L (2020) Idrid: Diabetic retinopathy-segmentation and grading challenge. Medical image analysis 59:101561
Raghu, M., Zhang, C., Kleinberg, J., Bengio, S.: Transfusion: Understanding transfer learning for medical imaging. In: Advances in Neural Information Processing Systems, pp. 3347–3357 (2019)
Raschka S, Kaufman B (2020) Machine learning and ai-based approaches for bioactive ligand discovery and gpcr-ligand recognition. Methods 180:89–110
Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D (2019) Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 126(4):552–564
Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D et al (2019) Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 126(4):552564
Selvachandran, G., Quek, S.G., Paramesran, R., Ding, W., Son, L.H.: Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artificial Intelligence Review, 1–50 (2022)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Shi, X., Cao, W., Raschka, S.: Deep neural networks for rank-consistent ordinal regression based on conditional probabilities. arXiv preprint http://arxiv.org/abs/2111.08851arXiv:2111.08851 (2021)
Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. Journal of big data 6(1):1–48
Shurrab, S., Duwairi, R.: Self-supervised learning methods and applications in medical imaging analysis: A survey. arXiv preprint http://arxiv.org/abs/2109.08685arXiv:2109.08685 (2021)
Sikder N, Masud M, Bairagi AK, Arif ASM, Nahid A-A, Alhumyani HA (2021) Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images. Symmetry 13(4):670
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint http://arxiv.org/abs/1409.1556arXiv:1409.1556 (2014)
Stitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, Gardiner TA, Lyons TJ, Hammes H-P, Simo R (2016) The progress in understanding and treatment of diabetic retinopathy. Progress in retinal and eye research 51:156–186
Sun, R., Li, Y., Zhang, T., Mao, Z., Wu, F., Zhang, Y.: Lesion-aware transformers for diabetic retinopathy grading. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10938–10947 (2021)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Tammina S (2019) Transfer learning using vgg-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications (IJSRP) 9(10):143–150
Tobin, J., Karayev, S., Abbeel, P.: Troubleshooting Deep Neural Networks. OpenAI (2019)
Truong, T., Mohammadi, S., Lenga, M.: How transferable are self-supervised features in medical image classification tasks? In: Machine Learning for Health, pp. 54–74 (2021). PMLR
Tymchenko, B., Marchenko, P., Spodarets, D.: Deep learning approach to diabetic retinopathy detection. arXiv preprint http://arxiv.org/abs/2003.02261arXiv:2003.02261 (2020)
Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers & Electrical Engineering 72:274–282
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)
Wang, Z., Yang, J.: Diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation. In: Workshops at the Thirty-second AAAI Conference on Artificial Intelligence (2018)
Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149:708–717
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Yang, Y., Li, T., Li, W., Wu, H., Fan, W., Zhang, W.: Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 533–540 (2017). Springer
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflicts of interest
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ohri, K., Kumar, M. Supervised fine-tuned approach for automated detection of diabetic retinopathy. Multimed Tools Appl 83, 14259–14280 (2024). https://doi.org/10.1007/s11042-023-16049-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-16049-7