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Recent advances in deep learning have often surpassed human performance in image classification. Among the most renowned cases, just think of the ImageNet Large Scale Visual Recognition Challenge competition. However, challenges persist in complex fields such as medical imaging. An example is the Human Protein Atlas which maps all human proteins in more than 171,000 images that makes a computation challenge due to high class imbalance. To address these challenges from a green perspective, we propose a transfer learning approach using Convolutional Neural Networks (CNNs) pre-trained on the ImageNet dataset. We use CNN layers as feature extractors, feeding the extracted features into a Support Vector Machine with a linear kernel. Our method combines both image-level and cell-level perspectives. Furthermore, at the cell level, we segment nuclei and extract the surrounding nuclear membrane area. The combination of the two perspectives shows promising classification performance with limited computational effort.

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Transfer Learning Approach for High-Imbalance and Multi-class Classification of Fluorescence Images

  • 会议论文

  • 发表日期：

    2025年

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  • Vincenzo Taormina,
  • Domenico Tegolo,
  • Cesare Valenti

摘要

Recent advances in deep learning have often surpassed human performance in image classification. Among the most renowned cases, just think of the ImageNet Large Scale Visual Recognition Challenge competition. However, challenges persist in complex fields such as medical imaging. An example is the Human Protein Atlas which maps all human proteins in more than 171,000 images that makes a computation challenge due to high class imbalance. To address these challenges from a green perspective, we propose a transfer learning approach using Convolutional Neural Networks (CNNs) pre-trained on the ImageNet dataset. We use CNN layers as feature extractors, feeding the extracted features into a Support Vector Machine with a linear kernel. Our method combines both image-level and cell-level perspectives. Furthermore, at the cell level, we segment nuclei and extract the surrounding nuclear membrane area. The combination of the two perspectives shows promising classification performance with limited computational effort.