CFP last date
22 December 2025
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 22 December 2025

Submit your paper
Know more
The week's pick
Responsive image
A Hybrid Transformer-CNN Framework with Early and Late Fusion for Robust Skin Lesion Classification

Raihan Tanvir
Random Articles
Reseach Article

Selective Feature Representation in Prototypical Networks for Medical Image Classification

by Ranjana Roy Chowdhury, Deepti R. Bathula
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 36
Year of Publication: 2025
Authors: Ranjana Roy Chowdhury, Deepti R. Bathula
10.5120/ijca2025925622

Ranjana Roy Chowdhury, Deepti R. Bathula . Selective Feature Representation in Prototypical Networks for Medical Image Classification. International Journal of Computer Applications. 187, 36 ( Sep 2025), 55-62. DOI=10.5120/ijca2025925622

@article{ 10.5120/ijca2025925622,
author = { Ranjana Roy Chowdhury, Deepti R. Bathula },
title = { Selective Feature Representation in Prototypical Networks for Medical Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2025 },
volume = { 187 },
number = { 36 },
month = { Sep },
year = { 2025 },
issn = { 0975-8887 },
pages = { 55-62 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number36/selective-feature-representation-in-prototypical-networks-for-medical-image-classification/ },
doi = { 10.5120/ijca2025925622 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-09-23T00:35:09+05:30
%A Ranjana Roy Chowdhury
%A Deepti R. Bathula
%T Selective Feature Representation in Prototypical Networks for Medical Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 36
%P 55-62
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Prototypical Networks operate by embedding both support and query samples into a common feature space and then representing each class with the mean vector of its support embeddings. Yet, the inherent complexity of medical imagery pose significant challenges for isolating features that are both precise and dependable. Consequently, constructing effective prototypes in this domain demands not only sophisticated preprocessing and more powerful embedding architectures, but also deliberate refinement of feature representations. In this context, most important and representative feature map selection is critical. We introduce Selective Feature Representation in Prototypical Networks, a lightweight yet effective enhancement to prototype-based few-shot learning. Our approach explicitly refines support embeddings by ranking and selecting the top feature maps for each class, leveraging an ensemble of channelwise statistics—Global Average Pooling, Max Pooling, and Variance. Built on a compact CONV4 backbone, our method outperforms much larger state-of-the-art models on two medical benchmarks: achieving 67.18% (1-shot) and 78.20% (5-shot) on Derm7pt skin-lesion classification, and 63.39% (1-shot), 77.17% (5-shot), and 83.06% (10-shot) on BloodMNIST pathology classification. These gains demonstrate that targeted feature-map selection significantly improves prototype quality and generalization with minimal complexity, offering a practical solution for resource-constrained clinical applications.

References
  1. Andrea Acevedo, Anna Merino, Santiago Alferez,´ Angel´ Molina, Laura Boldu, and Jos´ e Rodellar. A dataset of micro-´ scopic peripheral blood cell images for development of automatic recognition systems. Data in Brief, 30:105474, 2020.
  2. Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, Stephane Pateux, and Vincent Gripon.´ Easy—ensemble augmented-shot-y-shaped learning: Stateof-the-art few-shot classification with simple components. Journal of Imaging, 8(7):179, 2022.
  3. Arijit Bera, Subhransu Kumar, and Ashwin Machanavajjhala. Exploring medical image mining using deep convolutional networks. In SIAM International Conference on Data Mining, pages 543–551, 2018.
  4. Runyang Cao, Yu Zhang, Song Bai, and Philip Torr. Comet: Concept-based prototypical networks for few-shot learning. In International Joint Conference on Artificial Intelligence, pages 2108–2114, 2021.
  5. Yujia Chen, Zhenguo Li, and Ruijia Wang. Feature-level meta-learning with attention for few-shot classification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 512–520, 2022.
  6. Jessica Deuschel, Daniel Firmbach, Carol I. Geppert, Markus Eckstein, Arndt Hartmann, Volker Bruns, Petr Kuritcyn, Jakob Dexl, David Hartmann, Dominik Perrin, Thomas Wittenberg, and Michaela Benz. Multi-prototype few-shot learning in histopathology. In IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Montreal, BC, Canada, October 11-17, 2021, pages 620–628. IEEE.
  7. Arnout Devos, Sylvain Chatel, and Matthias Grossglauser. Reproducing meta-learning with differentiable closed-form solvers. In Reproducibility in Machine Learning, ICLR 2019 Workshop, New Orleans, Louisiana, United States, May 6, 2019. OpenReview.net.
  8. Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun. Dermatologist-level classification of skin cancer with deep neural networks. In Nature, pages 115–118, 2017.
  9. J. Kawahara et al. Seven-point checklist and skin lesion classification. IEEE JBHI, 2019.
  10. K. Mahajan et al. Meta-dermdiagnosis: Few-shot skin disease identification. In CVPR Workshops, 2020.
  11. Chelsea Finn, Pieter Abbeel, and Sergey Levine. Modelagnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning, pages 1126–1135, 2017.
  12. Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Franc¸ois Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks. In International Conference on Machine Learning, pages 1180–1189, 2016.
  13. Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard E. Turner. Decision-theoretic metalearning: Versatile and efficient amortization of few-shot learning. CoRR, abs/1805.09921, 2018.
  14. Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard E. Turner. Decision-theoretic metalearning: Versatile and efficient amortization of few-shot learning. CoRR, abs/1805.09921, 2018.
  15. Xia He, Xue Yuan, Kai Zhang, Hu Li, and Wei Liu. Robust re-weighting prototypical networks for few-shot classification. In Proceedings of the 6th International Conference on Robotics and Artificial Intelligence (ICRAI ’20), pages 125– 132, Singapore, 2020. ACM.
  16. Timothy M. Hospedales, Antreas Antoniou, Paul Micahelis, and Amos Storkey. Meta-learning in neural networks: A survey. In International Conference on Learning Representations, 2020.
  17. Jieneng Hu and et al. Feature disentanglement and filtering for few-shot learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2023.
  18. Yuqing Hu, Vincent Gripon, and Stephane Pateux. Leveraging´ the feature distribution in transfer-based few-shot learning. In International conference on artificial neural networks, pages 487–499. Springer, 2021.
  19. Xiao Jia, Yuling Su, and Hong Zhao. Few-shot learning via relation network based on coarse-grained granulation. Appl. Intell., 53(1):996–1008.
  20. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pages 1097–1105, 2012.
  21. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. In Proceedings of the 28th International Conference on Neural Information Processing Systems, pages 1–9, 2015.
  22. Qi Li, Hao Zhang, and Wei Wang. Learning to select informative features for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11835–11844, 2023.
  23. Shuhan Li, Xiaomeng Li, Xiaowei Xu, and Kwang-Ting Cheng. Dynamic subcluster-aware network for few-shot skin disease classification. IEEE Trans. Neural Networks and Learning Systems, 36(1):1872–1883, 2025.
  24. Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud A. A. Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, and Clara I. Sanchez. A survey on deep learning in medical image anal-´ ysis. In Medical Image Computing and Computer-Assisted Intervention – MICCAI, pages 295–307, 2017.
  25. Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, and Han Hu. Negative margin matters: Understanding margin in few-shot classification. In European conference on computer vision, pages 438–455. Springer, 2020.
  26. Xiaolong Liu, Yifan Wang, and Lei Zhang. Weighted prototypical networks for few-shot learning. In AAAI Conference on Artificial Intelligence, pages 9319–9326, 2019.
  27. Yun Liu, Geert Litjens, and Bram van Ginneken. Deep learning for content-based medical image retrieval: A performance study. In 2016 IEEE International Conference on Image Processing (ICIP), pages 1275–1279, 2016.
  28. Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan. Learning transferable features with deep adaptation networks. In International Conference on Machine Learning, pages 97–105, 2015.
  29. Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, and Jingkuan Song. A closer look at few-shot classification again. In International Conference on Machine Learning, pages 23103–23123. PMLR, 2023.
  30. Puneet Mangla, Nupur Kumari, Abhishek Sinha, Mayank Singh, Balaji Krishnamurthy, and Vineeth N Balasubramanian. Charting the right manifold: Manifold mixup for few-shot learning. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 2218– 2227, 2020.
  31. Alfonso Medela, Artzai Picon, Cristina L. Saratxaga, Oihana Belar, Virginia Cabezon, Riccardo Cicchi, Roberto Bilbao,´ and Ben Glover. Few shot learning in histopathological images:reducing the need of labeled data on biological datasets. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pages 1860–1864, 2019.
  32. Zeynep Ozdemir, Hacer Yalim Keles, and¨ Omer¨ Ozg¨ ur¨ Tanrıover. Meta-transfer derm-diagnosis: Exploring few-shot¨ learning and transfer learning for skin disease classification in long-tail distribution. arXiv preprint arXiv:2404.16814, 2024.
  33. Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals. Rapid learning or feature reuse? towards understanding the effectiveness of MAML. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net.
  34. Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. In ICLR Workshop, 2017.
  35. Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. Meta-weight-net: Learning an explicit mapping for sample weighting. In Advances in Neural Information Processing Systems, NeurIPS 2019, pages 1917– 1928, 2019.
  36. Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In Advances in Neural Information Processing Systems, pages 4077–4087, 2017.
  37. Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Tat-Seng Chua, and Bernt Schiele. Meta-transfer learning through hard tasks. IEEE Trans. Pattern Anal. Mach. Intell., 44(3):1443–1456, 2022.
  38. Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales. Learning to compare: Relation network for few-shot learning. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 1199–1208. Computer Vision Foundation / IEEE Computer Society.
  39. Koichi Suzuki, Katsuyuki Mori, and Kunio Doi. Medical image sorting and retrieval by combining shape and texture features. In SPIE Medical Imaging, pages 978–985, 2016.
  40. Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. Matching networks for one-shot learning. In Advances in Neural Information Processing Systems, pages 3630–3638, 2016.
  41. Yongqiang Wang, Yikang Li, Kai Chen, Shiji Song, and Ying Huang. A survey on few-shot learning. In CVPR Workshops, pages 2866–2875, 2020.
  42. Zhirong Wu, Alexei A Efros, and Stella X Yu. Improving generalization via scalable neighborhood component analysis. In Proceedings of the european conference on computer vision (ECCV), pages 685–701, 2018.
  43. Zhirong Wu, Alexei A Efros, and Stella X Yu. Improving generalization via scalable neighborhood component analysis. In Proceedings of the european conference on computer vision (ECCV), pages 685–701, 2018.
  44. Jiawei Yang, Hanbo Chen, Jiangpeng Yan, Xiaoyu Chen, and Jianhua Yao. Towards better understanding and better generalization of low-shot classification in histology images with contrastive learning. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net.
  45. Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems, pages 3320–3328, 2014.
  46. Zhongzheng Yue, Qianru Sun, and Qi Yu. Compact feature learning for prototypical few-shot classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 10454–10463, 2023.
  47. Fei Zhou, Peng Wang, Lei Zhang, Wei Wei, and Yanning Zhang. Revisiting prototypical network for cross domain few-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 20061–20070. IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Meta Learning Few Shot Learning Prototypical Networks Feature Map Selection

Powered by PhDFocusTM