通过分阶段分解类别特征，提升医学图像分类准确率
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification
- 按类别粒度从细到粗反向训练，模拟人类学习过程
- 在4个医学数据集上最高达99.45%准确率，优于传统方法
- 适合处理数据不均衡的医疗图像分类任务
课程学习在机器学习中被证明有效，但在医学图像领域因数据不规则常导致类别混淆。本文提出CLOG-CD方法，结合课程学习与类别分解策略，利用预训练模型ResNet-50和DenseNet-121作为骨干网络，在四个不平衡医学图像数据集（胸部X光、脑肿瘤、数字膝关节X光、结直肠癌病理）上进行验证。该方法基于类内粒度分解权重，采用由细到粗的反向训练顺序（即反课程学习）。实验结果表明，使用ResNet-50时，对胸片、脑肿瘤、膝关节和结直肠癌数据集的准确率分别达到96.08%、96.91%、79.76%、99.17%；使用DenseNet-121时分别为94.86%、94.63%、76.19%、99.45%，均优于其他训练策略。
原文摘要 · Abstract (English)
Curriculum learning strategies have been proven to be effective in various applications and have gained significant interest in the field of machine learning. It has the ability to improve the final model's performance and accelerate the training process. However, in the medical imaging domain, data irregularities can make the recognition task more challenging and usually result in misclassification between the different classes in the dataset. Class-decomposition approaches have shown promising results in solving such a problem by learning the boundaries within the classes of the data set. In this paper, we present a novel convolutional neural network (CNN) training method based on the curriculum learning strategy and the class decomposition approach, which we call CLOG-CD, to improve the performance of medical image classification. We evaluated our method on four different imbalanced medical image datasets, such as Chest X-ray (CXR), brain tumour, digital knee X-ray, and histopathology colorectal cancer (CRC). CLOG-CD utilises the learnt weights from the decomposition granularity of the classes, and the training is accomplished from descending to ascending order (i.e., anti-curriculum technique). We also investigated the classification performance of our proposed method based on different acceleration factors and pace function curricula. We used two pre-trained networks, ResNet-50 and DenseNet-121, as the backbone for CLOG-CD. The results with ResNet-50 show that CLOG-CD has the ability to improve classification performance with an accuracy of 96.08% for the CXR dataset, 96.91% for the brain tumour dataset, 79.76% for the digital knee X-ray, and 99.17% for the CRC dataset, compared to other training strategies. In addition, with DenseNet-121, CLOG-CD has achieved 94.86%, 94.63%, 76.19%, and 99.45% for CXR, brain tumour, digital knee X-ray, and CRC datasets, respectively
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