FBENet: Feature-Level Boosting Ensemble Network for Hashimoto's Thyroiditis Ultrasound Image Classification.

Distinguishing Hashimoto's thyroiditis (HT) lesions from ordinary thyroid tissues is difficult with ultrasound images. Challenges in achieving high performance of HT ultrasound image classification include the low resolution, blurred features and large area of irrelevant noise. To address these problems, we propose a Feature-level Boosting Ensemble Network (FBENet) for HT ultrasound image classification. Specifically, to capture the features of suspicious HT lesions efficiently, an Ensemble Feature Boosting Module (EFBM) is introduced into the feature-level ensemble to boost the blurred features. Then, the spatial attention mechanism is adopted in backbone models to improve the feature focusing performance and representation ability. Furthermore, feature-level ensemble technique is employed in the training process to achieve more comprehensive feature representation ability. Experimentally, FBENet was trained on 6,503 HT ultrasound images, and tested on 1,626 HT ultrasound images with 82.92% accuracy and 89.24% AUC on average.

HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAM.

The early lesions of Hashimoto's thyroiditis are inconspicuous, and the ultrasonic features of these early lesions are indistinguishable from other thyroid diseases. This paper proposes a Hashimoto Thyroiditis ultrasound image classification model HT-RCM which consists of a Residual Full Convolution Transformer (Res-FCT) model and a Residual Channel Attention Module (Res-CAM). To collect the low-order information caused by hypoechoic signals accurately, the residual connection is injected between FCTs to form Res-FCT which helps HT-RCM superimpose the low-order input information and high-order output information together. Res-FCT can make HT-RCM focus more on hypoechoic information while avoiding gradient dispersion. The initial feature map is inserted into Res-FCT again through a down-sampling component, which further helps HT-RCM exact multi-level original semantic information in the ultrasound image. Res-CAM is constructed by implementing a residual connection between a channel attention module and a convolution layer. Res-CAM can effectively increase the weights of the lesion channels while suppressing the weights of the noise channels, which makes HT-RCM focus more on the lesion regions. The experimental results on our collected dataset show that HT-RCM outperforms the mainstream models and obtains state-of-the-art performance in HT ultrasound image classification.

HTC-Net: Hashimoto's thyroiditis ultrasound image classification model based on residual network reinforced by channel attention mechanism.

Convolutional neural network (CNN) is efficient in extracting and aggregating local features in the spatial dimension of the images. However, obtaining the inapparent texture information of the low-echo area in the ultrasound images is not easy, and it is especially challenging for the early lesion recognition in Hashimoto's thyroiditis (HT) ultrasound images. In this paper, a HT ultrasound image classification model HTC-Net based on residual network reinforced by channel attention mechanism is proposed. HTC-Net strengthens the features of the important channels by reinforced channel attention mechanism through which the high-level semantic information is enchanced and the low-level semantic information is suppressed. Residual network assists HTC-Net focus on the key local areas of the ultrasound images while pay attention to the global semantic information. Furthermore, in order to solve the problem of uneven distribution caused by large amount of difficult-to-classify samples in the data sets, a new feature loss function TanCELoss with weight factor dynamically adjusting is constructed. TanCELoss function can better assist HTC-Net to transform difficult-to-classify samples into easy-to-classify samples gradually, and improve the balancing distribution of the samples. The experiments are implemented based on data sets collected by the Endocrinology Department of four branches from Guangdong Provincial Hospital of Chinese Medicine. Both quantitative testing and visualization results show that HTC-Net obtains STOA performance for early lesions recognition in HT ultrasound images. HTC-Net has great application value especially under the condition of owning only small data samples.