With the increasing cases of thyroid malignant tumors, the diagnosis of thyroid nodule has attracted more and more attention. Deep learning has achieved promising results in computer-aided diagnosis due to the advantages of obtaining high-dimensional features. In this paper, we proposed a hybrid multi-branch convolutional neural network based on feature cropping method for feature extraction and classification of thyroid nodule ultrasound images. Firstly, we designed a backbone convolutional neural network to extract shared feature maps and a classification network as global branch. Next, we added a feature cropping branch in the network to perform multi-cropping on batch feature maps, to reduce the impact on classification caused by the similarity of local features between benign and malignant thyroid nodule images. Finally, based on softmax predictions of different branch feature maps, we utilize a weighted cross-entropy loss function to train our proposed binary-classification network. Experimental results show that our proposed method has achieved 96.13% accuracy, 93.24% precision, 97.18% recall, and 95.17% F1-measure in public dataset and local dataset, outperforming other models. INDEX TERMS Thyroid nodule classification, ultrasound image, hybrid multi-branch network, feature cropping, weighted loss function.
Diffusion-weighted magnetic resonance imaging (DWI) is sensitive to acute ischemic stroke and is a common diagnostic method for the stroke. However, the diagnostic result relies on the visual observation of neurologists which may vary from doctor to doctor under different circumstance. And manual segmentation is often a time-consuming and subjective process. The time from onset to thrombus removal has a significant impact on the prognosis of patients with acute ischemic stroke. The shorter the time, the better the prognosis. For this purpose we present a novel framework to quickly and automatically segment the ischemic stroke lesions on DWI. We mainly have three contributions: firstly, we design a detection and segmentation network (DSN) to solve the two kinds of data imbalance; secondly, we propose a triple-branch DSN architecture, used for extracting different plane feature respectively; thirdly, we propose a multi-plane fusion network (MPFN), which aims to make final prediction more accurate. Extensive experiments on ISLES2015 SSIS DWI sequence dataset demonstrate the superiority of our proposed segmentation method. The dice reached 62.2% and the sensitivity reached 71.7%.
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