The Influence of the Shape Symbol Paired Grouping Gradient Mean Iterative Method (SSPGG-IM) on the Medical Image Classification Problem in the Context of Small Samples

DOI

10.2991/978-94-6239-648-7_82How to use a DOI?

Keywords

Artificial Intelligence; Small Sample Learning; Medical Imaging

Abstract

This study focuses on the analysis of small sample medical images. It first reviews the research value and application status of small sample learning in this field, as well as the limitations of traditional methods. The research is based on three types of core small-sample learning methods as the technical foundation: computationally efficient metric learning (such as Matching Networks, Prototypical Networks), adaptable meta-learning (such as MAML), and enhancement methods that can alleviate the problem of insufficient samples. This experiment was verified using the PCam dataset, which originated from the breast cancer histopathology database and contained 327,680 96 × 96 pixel pathological slice images. These images were labeled by professional pathologists as “containing cancer cells (Positive)” or “not containing cancer cells (Negative)” to provide high-quality data support for model training and evaluation. To enhance the generalization ability of the model, an innovative parameter iteration mechanism was introduced in the research: During each iteration, the connections are randomly grouped in pairs based on their shapes and the signs of the gradients. Each connection is iterated according to the original gradient to balance the influence of noise. Through theoretical analysis and experimental verification, the paper explores the application effect of small sample learning technology in medical image analysis, and promote the clinical transformation and practical application of this technology.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Zhengyang Li
AU  - Jiayi Zhang
PY  - 2026
DA  - 2026/04/24
TI  - The Influence of the Shape Symbol Paired Grouping Gradient Mean Iterative Method (SSPGG-IM) on the Medical Image Classification Problem in the Context of Small Samples
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 755
EP  - 760
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_82
DO  - 10.2991/978-94-6239-648-7_82
ID  - Li2026
ER  -