Thyroid nodules are found in 19% to 67% of individuals who are screened for thyroid cancer using ultrasonography. The large number of individuals examined for thyroid cancer is placing significant stress on radiologists and the healthcare system. Video-scale detection requires more training data than frame-scale detection. To alleviate the need for large-scale labeled datasets, a patch-scale self-supervised pre-training model was trained on unlabeled data to extract patch-distinguishing features, which are crucial for object detection. The pre-trained model was transferred to the video-based model to improve the performance of nodule detection. Experimental test results on 22 ultrasound videos containing 47 nodules show that the performance of our proposed method is 0.523 for mAP@50 and 0.430 for HOTA. The proposed method can process at 23 fps, which can meet the requirements of real-time tracking in screening scenarios.
Compared with the traditional EM clustering algorithm, the EMBoost clustering algorithm can improve two problems
that the sensitive result to initial value and the low precision. However, an important factor, the local information, is not
considered in the EMBoost algorithm, which is useful to enhance the performance of the EMBoost algorithm, especially
for image segmentation. We believe that neighbor pixels to the center measured by the space distance and the texture
distance are beneficial to the internal consistency of the homogeneous region. Hence, we proposed a new approach that
spatial information is brought into EMBoost clustering algorithm, which consisted of the adjacent pixels relative position
and the neighbor texture distance, in order to improve the performance EMBoost clustering method. According to the
experimental results of the texture image segmentation and the Synthetic Aperture Radar (SAR) image segmentation, the
proposed method can obtain better accuracy and visual effect, compared against other methods.
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