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https://www.selleckchem.com/products/SB-203580.html Breast cancer is the most common cancer among women worldwide. Medical ultrasound imaging is one of the widely applied breast imaging methods for breast tumors. Automatic breast ultrasound (BUS) image segmentation can measure the size of tumors objectively. However, various ultrasound artifacts hinder segmentation. We proposed an attention-supervised full-resolution residual network (ASFRRN) to segment tumors from BUS images. In the proposed method, Global Attention Upsample (GAU) and deep supervision were introduced into a full-resolution residual network (FRRN), where GAU learns to merge features at different levels with attention for deep supervision. Two datasets were employed for evaluation. One (Dataset A) consisted of 163 BUS images with tumors (53 malignant and 110 benign) from UDIAT Centre Diagnostic, and the other (Dataset B) included 980 BUS images with tumors (595 malignant and 385 benign) from the Sun Yat-sen University Cancer Center. The tumors from both datasets were manually segmented by mumors from BUS images. It achieved high segmentation accuracy with a reduced parameter number. We proposed ASFRRN, which combined with FRRN, attention mechanism, and deep supervision to segment tumors from BUS images. It achieved high segmentation accuracy with a reduced parameter number.Hynobiidae are a clade of salamanders that diverged early within the crown radiation and that retain a considerable number of features plesiomorphic for the group. Their evolutionary history is informed by a fossil record that extends to the Middle Jurassic Bathonian time. Our understanding of the evolution within the total group of Hynobiidae has benefited considerably from recent discoveries of stem hynobiids but is constrained by inadequate anatomical knowledge of some extant forms. Pseudohynobius is a derived hynobiid clade consisting of five to seven extant species living endemic to southwestern China. Although this clade has

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