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Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures

arXiv 2404.06080 ↗First posted 9 Apr 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

This study presents a computer-aided diagnosis (CAD) system to assist early detection of lung metastases during endobronchial ultrasound (EBUS) procedures, significantly reducing follow-up time and enabling timely treatment. Due to limited cytology images and morphological similarities among cells, classifying lung metastases is challenging, and existing research rarely targets this issue directly.To overcome data scarcity and improve classification, the authors propose a few-shot learning model using a hybrid pretrained backbone with fine-grained classification and contrastive learning. Parameter-efficient fine-tuning on augmented support sets enhances generalization and transferability. The model achieved 49.59% accuracy, outperforming existing methods. With 20 image samples, accuracy improved to 55.48%, showing strong potential for identifying rare or novel cancer types in low-data clinical environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 35 references that are in the map. First posted on arXiv 9 Apr 2024.

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References · 12

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  2. 2016Matching Networks for One Shot Learningcited by 2,246
  3. 2021CvT: Introducing Convolutions to Vision Transformerscited by 946
  4. 2021CoAtNet: Marrying Convolution and Attention for All Data Sizescited by 566
  5. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  6. 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 123
  7. 2023Are Natural Domain Foundation Models Useful for Medical Image Classification?cited by 44
  8. 2022SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classificationcited by 34
  9. 2022Few-Shot Classification with Contrastive Learningcited by 31
  10. 2023Context-Aware Meta-Learningcited by 20
  11. 2020Spatial Contrastive Learning for Few-Shot Classificationcited by 17
  12. 2019Learning with less data via Weakly Labeled Patch Classification in Digital Pathologycited by 8

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