Pre-training on Grayscale ImageNet Improves Medical Image Classification
Paper:http://openaccess.thecvf.com/content_ECCVW_2018/papers/11134/Xie_Pre-training_on_Grayscale_ImageNet_Improves_Medical_Image_Classification_ECCVW_2018_paper.pdf
Tips：ECCV2018的一篇paper。
（閱讀筆記）
1. Main idea
- 提出問題，醫學圖像的獲取很困難。large medical image datasets appropriate for training deep neural network models from scratch are difficult to assemble due to privacy restrictions and expert ground truth requirements.
- 一般的解決方法即是通過預訓練一個其他的模型再微調。to pre-train models on large datasets in other domains, such as ImageNet for classification of natural images.
- 再一次提出問題，Image-Net的圖像都是三通道，而醫學圖像一般都是灰度單通道圖像。However, ImageNet contains color images, which introduces artefacts and inefficiencies into models that are intended for single-channel medical images.
- 所以提出了本文的idea。we pre-trained an Inception-V3 model on ImageNet after converting the images to grayscale through a common transformation.
2. Method
把Image-Net的圖像轉換成灰度圖像後，用相同的參數喂入網絡(僅改變通道數爲1)，得到預訓練的模型，然後微調後實驗效果很好。
