Robust Few Shot Biological Pathology Classification via Optimized Contrastive MobileNetV2: A Transferable Model for Low Resource Medical Imaging

Nurul Adi Prawira(1), Muhammad Firmansyah(2*), Dhendra Marutho(3), Achraf Ouhab(4)


(1) Universitas Nusa Mandiri
(2) Universitas Nusa Mandiri
(3) Universitas Muhammadiyah Semarang
(4) University of Poitiers
(*) Corresponding Author

Abstract


Artificial intelligence has revolutionized computational diagnostics, however deploying reliable intelligent systems in extreme low-resource environments remains a critical structural challenge in health informatics. Conventional deep learning architectures, such as standard Convolutional Neural Networks (CNNs), are inherently data-hungry, making them prone to severe overfitting and catastrophic generalization failures when applied to rare biological pathologies. To overcome this limitation, we propose an Optimized Contrastive MobileNetV2 architecture embedded within a Few-Shot Learning (FSL) framework. By mathematically modifying the latent space representation using a contrastive loss function, the proposed model learns discriminative metric distances rather than relying on massive raw feature memorization. To rigorously validate the algorithm, we utilize a highly constrained dataset comprising merely 120 biological pathogen samples as a cross-domain proxy testbed, accurately simulating the extreme visual complexity and data scarcity typical of rare medical diagnostic scenarios. Extensive episodic evaluations demonstrate that the proposed methodology significantly outperforms conventional baselines. Under a 10-shot learning paradigm, the contrastive architecture achieved a macro-averaged accuracy of 89.2% and an F1-Score of 89.3%, remaining statistically robust against stochastic variations (p < 0.001). Furthermore, the integration of depthwise separable convolutions restricts the model complexity to approximately 3.4 × 10^6 parameters. Crucially, empirical evaluations confirm that this framework occupies merely 13.5 MB of physical storage and achieves an ultra-low inference latency of 12.5 ms per image. Ultimately, this study establishes a highly transferable, computationally efficient algorithmic model ready for seamless integration into intelligent clinical decision support systems and remote edge-computing health architectures.

Keywords


Few-Shot Learning; Contrastive Learning; MobileNetV2; Health Informatics; Edge Computing; Cross-Domain Proxy

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References


Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Medical image analysis. 2017;42:60-88. Available from: https://doi.org/ 10.1016/j.media.2017.07.005.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. Available from: https: //doi.org/10.1038/nature21056.

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019;25(1):44-56. Available from: https://doi.org/10.1038/s41591-018-0300-7.

Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nature Medicine. 2022;28(1):31-8. Available from: https://doi.org/10.1038/s41591-021-01614-0.

Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data. 2021;8(1):53. Available from: https://doi.org/10.1186/s40537-021-00444-8.

Zhang K, Zhou R, Adhikarla E, Yan Z, Liu Y, Yu J, et al. A generalist vision–language foundation model for diverse biomedical tasks. Nature medicine. 2024;30(11):3129-41. Available from: https: //doi.org/10.1038/s41591-024-03185-2.

Tajbakhsh N, Shin JY, Gurumurthy SR, Hurst AB, Kendall CB, Agrawal MB, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging. 2016;35(5):1299-312. Available from: https://doi.org/10.1109/TMI.2016.2535302.

Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Preparing medical imaging data for machine learning. European Radiology. 2020;30(5):2931-9. Available from: https: //doi.org/10.1148/radiol.2020192224.

Bhattacharya S, Maddikunta PKR, Pham QV, Gadekallu TR, Chowdhary CL, Alazab M, et al. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustainable Cities and Society. 2021;65:102589. Available from: https://doi.org/10.1016/j.scs.2020.102589.

Lu L, Zheng Y, Carneiro G, Yang L. Transfer learning with limited annotated data for small lesion segmentation in breast MRI. Medical Image Analysis. 2022;82:102454. Available from: https://doi. org/10.1016/j.media.2022.102454.

Zhou Y, Zhou F, Xi F, Liu Y, Peng Y, Carlson DE, et al. Efficient few-shot medical image segmentation via self-supervised variational autoencoder. Medical Image Analysis. 2025;104:103637. Available from: https://doi.org/10.1016/j.media.2025.103637.

Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17. Red Hook, NY, USA: Curran Associates Inc.; 2017. p. 4080–4090. Available from: https://dl.acm.org/doi/abs/10. 5555/3294996.3295163.

Wang Y, Yao Q, Kwok JT, Ni LM. Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (CSUR). 2020;53(3):1-34. Available from: https://doi.org/10.1145/3386252.

Raj R, Tiwari P, Gourisaria MK, Das H. Efficient object detection and labeling in retail environments using MobileNetV2 with inverted residuals. In: 2023 OITS International Conference on Information Technology (OCIT). IEEE; 2023. p. 634-9. Available from: https://doi.org/10.1109/OCIT59427. 2023.10430647.

Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim WK, Kang JJ. Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors. 2021;21(8):2852. Available from: https://doi.org/10.3390/s21082852.

Li X, et al. A Survey of Few-Shot Learning for Biomedical Time Series. IEEE Reviews in Biomedical Engineering. 2025. Available from: https://doi.org/10.1109/RBME.2024.3492381.

Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: International conference on machine learning. PMLR; 2020. p. 1597-607. Available from: https://dl.acm.org/doi/abs/10.5555/3524938.3525087.

Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, et al. Supervised contrastive learning. In: Advances in neural information processing systems. vol. 33; 2020. p. 18661-73. Available from: https://proceedings.neurips.cc/paper/2020/hash/ d89a66c7c80a29b1bdbab0f2a1a94af8-Abstract.html.

Le-Khac PH, Healy G, Smeaton AF. Contrastive representation learning: A framework and review. IEEE Access. 2020;8:193907-34. Available from: https://doi.org/10.1109/ACCESS.2020.3031549.

He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. p. 9729-38. Available from: https://doi.org/10.1109/CVPR42600.2020.00975.

Haghighi F, Taher M, Gotway M, Liang J. DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis. In: Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2022; 2022. p. 20792-802. Available from: https: //doi.org/10.1109/cvpr52688.2022.02016.

Zhang C, Zheng H, Gu Y. Dive into the details of self-supervised learning for medical image analysis. Medical Image Analysis. 2023;89:102879.

Shoaib M, Shah B, Ei-Sappagh S, Ali A, Ullah A, Alenezi F, et al. An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in plant science. 2023;14:1158933. Available from: https://doi.org/10.3389/fpls.2023.1158933.

Saleem MH, Potgieter J, Arif KM. Plant disease detection and classification by deep learning. Plants. 2020;9(11):1451. Available from: https://doi.org/10.3390/plants8110468.

Schmarje L, Santarossa M, Schröder SM, Koch R. A survey on semi-, self-, and unsupervised learning for image classification. IEEE Access. 2021;9:82146-68. Available from: https://doi.org/10.1109/ ACCESS.2021.3084358.

Cai TT, Ma R. Theoretical foundations of t-sne for visualizing high-dimensional clustered data. Journal of Machine Learning Research. 2022;23(301):1-54. Available from: https://dl.acm.org/doi/abs/ 10.5555/3586589.3586890.

Morid MA, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using ImageNet. Computers in Biology and Medicine. 2021;128:104115. Available from: https: //doi.org/10.1016/j.compbiomed.2020.104115.

Qayyum A, Qadir J, Bilal M, Al-Fuqaha A. Secure and robust machine learning for healthcare: A survey. IEEE Reviews in Biomedical Engineering. 2021;14:156-80. Available from: https://doi.org/10. 1109/RBME.2020.3013489.

Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making. 2020;20(1):310. Available from: https://doi.org/10.1186/s12911-020-01332-6.

Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health. 2021;3(11):e745-50. Available from: https: //doi.org/10.1016/S2589-7500(21)00208-9.

Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digital Medicine. 2020;3(1):119. Available from: https://doi.org/10.1038/ s41746-020-00323-1.

Li X, Jiang Y, Xia M, Zheng X, Rustam S, Sullivan R, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Data. 2020;7(1):257. Available from: https://doi.org/10.1038/s41598-020-69250-1.


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DOI: https://doi.org/10.26714/jichi.v7i1.20179

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Journal of Intelligent Computing and Health Informatics (JICHI)
 ISSN 2715-6923 (print) | 2721-9186 (online)
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Faculty of Engineering
Universitas Muhammadiyah Semarang

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