Annotated training data insufficiency remains to be one of the challenges of
applying deep learning in medical data classification problems. Transfer
learning from an already trained deep convolutional network can be used to
reduce the cost of training from scratch and to train with small training data
for deep learning. This raises the question of whether we can use transfer
learning to overcome the training data insufficiency problem in deep learning
based medical data classifications. Deep convolutional networks have been
achieving high performance results on the ImageNet Large Scale Visual
Recognition Competition (ILSVRC) image classification challenge. One example is
the Inception-V3 model that was the first runner up on the ILSVRC 2015
challenge. Inception modules that help to extract different sized features of
input images in one level of convolution are the unique features of the
Inception-V3. In this work, we have used a pretrained Inception-V3 model to
take advantage of its Inception modules for Diabetic Retinopathy detection. In
order to tackle the labelled data insufficiency problem, we sub-sampled a
smaller version of the Kaggle Diabetic Retinopathy classification challenge
dataset for model training, and tested the model's accuracy on a previously
unseen data subset. Our technique could be used in other deep learning based
medical image classification problems facing the challenge of labeled training
data insufficiency.

研究探讨了如何利用预训练的深度卷积神经网络 (Inception-V3) 中的 Inception 模块进行糖尿病视网膜病变检测，以解决深度学习在医学数据分类问题中的标记数据不足的挑战。通过利用 Kaggle 糖尿病视网膜病变分类挑战数据集的子集，作者训练了预训练 Inception-V3 模型，取得了不错的结果。该研究展示了在标记数据不足的情况下如何利用迁移学习来进行医学图像分类中的大规模筛选和准确性问题。