Research map: Efficient Few-Shot Learning for Medical Image Classification: A Practical Review of Lightweight and Resource-Aware Approaches

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Papers in this map

  1. Knowledge-Guided Semantic Transfer Network for Few-Shot Image Recognition · Zechao Li · 2023 · 124 citations · Cited by this paper
  2. Enhancing discharge location prediction for patients undergoing orthopedic surgery using side-tuning · 2026 · Related
  3. Cryptanalysis and Improvement of a Video Cryptosystem via Chaos and S-Box · Yiting Lin · 2026 · 51 citations · Cited by this paper
  4. KAMDA: A multi-view Kolmogorov-Arnold Network integrating pre-trained BERT embeddings and similarity-based imputation for microbe-drug association prediction · 2026 · Related
  5. From Sample Poverty to Rich Feature Learning: A New Metric Learning Method for Few-Shot Classification · Lei Zhang · 2024 · 43 citations · Cited by this paper
  6. Multi-dataset lung and colon cancer histopathology classification using hybrid CNN-vision transformer · 2026 · Related
  7. Pyramid Pixel Context Adaption Network for Medical Image Classification With Supervised Contrastive Learning · Xiaoqing Zhang · 2024 · 42 citations · Cited by this paper
  8. LiT-HiSegFormer-Net: A hybrid SegFormer network with Bi-level context fusion for liver and tumor segmentation in CT images · 2026 · Related
  9. Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms · Fatemeh Askari · 2025 · 40 citations · Cited by this paper
  10. Compact attention-driven deep network for interpretable oral squamous cell carcinoma screening · 2026 · Related
  11. A Distillation Approach to Transformer-Based Medical Image Classification with Limited Data · Aynur Sevinç · 2025 · 27 citations · Cited by this paper
  12. HyFormer-Net: A synergistic CNN-Transformer with interpretable multi-scale fusion for breast lesion segmentation and classification in ultrasound images · 2026 · Related
  13. Contrastive prototype network with prototype augmentation for few-shot classification · Mengjuan Jiang · 2024 · 23 citations · Cited by this paper
  14. Validation of webcam-based eye-tracking for clinically relevant paradigms: Saccade, attention bias, and free-viewing tasks · 2026 · Related
  15. Combining various training and adaptation algorithms for ensemble few-shot classification · Zhen Jiang · 2025 · 22 citations · Cited by this paper
  16. Applications of artificial intelligence in rare skin diseases and skin cancers: A scoping review · 2026 · Related
  17. Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network · Zijun Guo · 2024 · 20 citations · Cited by this paper
  18. Leveraging retinal vessel segmentation for improved disease classification · 2026 · Related
  19. Leveraging Bilateral Correlations for Multi-Label Few-Shot Learning · Yuexuan An · 2024 · 20 citations · Cited by this paper
  20. A Lightweight CNN–Transformer Framework for EEG Seizure Detection with Ablation, Interpretability, and Noise-Robustness Analysis · 2026 · Related
  21. PRSN: Prototype resynthesis network with cross-image semantic alignment for few-shot image classification · Mengping Dong · 2024 · 20 citations · Cited by this paper
  22. ProtoMed: Prototypical networks with auxiliary regularization for few-shot medical image classification · Achraf Ouahab · 2024 · 20 citations · Cited by this paper
  23. CDCNet: Cross-domain few-shot learning with adaptive representation enhancement · Xueying Li · 2025 · 20 citations · Cited by this paper
  24. Cross- and Intra-Image Prototypical Learning for Multi-Label Disease Diagnosis and Interpretation · Chong Wang · 2025 · 19 citations · Cited by this paper
  25. A genetic programming-based method for image classification with small training data · Qinglan Fan · 2023 · 17 citations · Cited by this paper
  26. A novel lightweight deep learning based approaches for the automatic diagnosis of gastrointestinal disease using image processing and knowledge distillation techniques · Zafran Waheed · 2024 · 17 citations · Cited by this paper
  27. Efficient CNN Accelerator Based on Low-End FPGA with Optimized Depthwise Separable Convolutions and Squeeze-and-Excite Modules · Jiahe Shen · 2025 · 17 citations · Cited by this paper
  28. Evaluating Machine Learning Techniques for Brain Tumor Detection with Emphasis on Few-Shot Learning Using MAML · Soham Sanjay Vaidya · 2025 · 17 citations · Cited by this paper
  29. Few-shot learning with representative global prototype · Yukun Liu · 2024 · 16 citations · Cited by this paper
  30. PrototypeFormer: Learning to explore prototype relationships for few-shot image classification · Meijuan Su · 2025 · 16 citations · Cited by this paper
  31. Learning from class-imbalanced data using misclassification-focusing generative adversarial networks · Jaesub Yun · 2023 · 15 citations · Cited by this paper
  32. Dynamic Subcluster-Aware Network for Few-Shot Skin Disease Classification · Shuhan Li · 2023 · 15 citations · Cited by this paper
  33. Prototype-Neighbor Networks with task-specific enhanced meta-learning for few-shot classification · Zhen Jiang · 2025 · 15 citations · Cited by this paper
  34. FAMF-Net: Feature Alignment Mutual Attention Fusion With Region Awareness for Breast Cancer Diagnosis via Imbalanced Data · Yiyao Liu · 2024 · 14 citations · Cited by this paper
  35. CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation · Xinyi Yang · 2024 · 13 citations · Cited by this paper
  36. Overall positive prototype for few-shot open-set recognition · Liang-Yu Sun · 2024 · 13 citations · Cited by this paper
  37. Few-shot learning with long-tailed labels · Hongliang Zhang · 2024 · 13 citations · Cited by this paper
  38. Adaptive Task-Aware Refining Network for Few-Shot Fine-Grained Image Classification · Liyun Yu · 2024 · 13 citations · Cited by this paper
  39. Few-shot based learning recaptured image detection with multi-scale feature fusion and attention · Israr Hussain · 2024 · 13 citations · Cited by this paper

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