# Semi-Supervised Medical Image Classification Based on Attention and Intrinsic Features of Samples
###### Abstract  
The training of deep neural networks usually requires a lot of high-quality data with good annotations to obtain good performance. However, in clinical medicine, obtaining high-quality marker data is laborious and expensive because it requires the professional skill of clinicians. In this paper, based on the consistency strategy, we propose a new semi-supervised model for medical image classification which introduces a self-attention mechanism into the backbone network to learn more meaningful features in image classification tasks and uses the improved version of local loss at the supervision loss to reduce the misclassification of samples. Finally, we add a consistency loss similar to the unsupervised consistency loss to encourage the model to learn more about the internal features of unlabeled samples. Our method achieved 94.02% AUC and 72.03% Sensitivity on the ISIC 2018 dataset and 79.74% AUC on the ChestX-ray14 dataset. These results show the effectiveness of our method in single-label and multi-label classification. semi-supervised learning; medical image classification; intrinsic characteristic relationship of samples; self-attention mechanism; self-ensembling model +
Footnote †: journal: Medical Image Classification and Intrinsic Features of Samples. [PERSON] et al. [15] constructed a graph model of chest disease diagnosis given extremely limited labeled data and assigned labels to unlabeled samples by the method of label propagation. (3) Consistency-based method [16]: [PERSON] et al. [17] improved the \(\pi\) model [18] and used it for a semi-supervised skin lesion segmentation and transformation consistency strategy. [PERSON] et al. [19; 20] strengthened the prediction consistency between the student model and the teacher model, and [PERSON] et al. [5] effectively improved the method based on consistency regularization by constraining the feature space to learn the features between different classes and different features within the same class. As for skin lesions, accurate classification is very important for clinical diagnosis. On account of the similarities among skin lesions, many studies have been carried out [21; 22; 23; 24] to solve this problem. Early research work [25] mainly collected features by hand, such as shape, color, texture, etc., to distinguish different types of lesions. However, recent research work mainly uses the remarkable feature extraction ability of the convolutional neural network to solve this problem. For example, [PERSON] et al. [21] integrated CNNs, sparse coding, and a support vector machine for melanoma identification and achieved good results. [PERSON] et al. [22] proposed a very deep network to distinguish melanoma from non-melanoma lesions. [PERSON] et al. [23] put forward a model to focus on semantically meaningful lesion areas using the self-attention ability of convolutional neural networks. In the context of chest disease diagnosis, the automatic diagnosis platform for ChestX-ray is also of far-reaching significance in clinical practice. The early research in this area was mainly based on manual classification, which largely depends on the quality of manually extracted features. With the emergence of large-scale datasets and the rise of deep neural networks, convolution neural networks have been used in recent research to solve this problem [24; 26]. For example, [PERSON] et al. [24] proved the effectiveness of using a multi-label learning framework to detect and even locate chest diseases from ChestX-ray samples. After the ChestX-ray14 dataset was published, [PERSON] et al. [27] proposed a more advanced model to detect 14 common chest diseases. However, these methods rely to a great extent on a large number of high-quality annotated data, and a semi-supervised model based on a graph recently proposed by [PERSON] et al. [24; 28] solves this problem well. In this paper, we propose a new semi-supervised model for medical image classification.