Abstract：
OBJECTIVE: In recent years, breast cancer has become the greatest threat to women. There are many studies dedicated to the precise segmentation of breast tumors, which is indispensable in computer-aided diagnosis. Deep neural networks have achieved accurate segmentation of images. However, convolutional layers are biased to extract local features and tend to lose global and location information as the network deepens, which leads to a decrease in breast tumors segmentation accuracy. For this reason, we propose a hybrid attention-guided network (HAG-Net). We believe that this method will improve the detection rate and segmentation of tumors in breast ultrasound images.
METHODS: The method is equipped with multi-scale guidance block (MSG) for guiding the extraction of low-resolution location information. Short multi-head self-attention (S-MHSA) and convolutional block attention module are used to capture global features and long-range dependencies. Finally, the segmentation results are obtained by fusing multi-scale contextual information.
RESULTS: We compare with 7 state-of-the-art methods on two publicly available datasets through five random fivefold cross-validations. The highest dice coefficient, Jaccard Index and detect rate ([Formula: see text]%, [Formula: see text]%, [Formula: see text]% and [Formula: see text]%, [Formula: see text]%, [Formula: see text]%, separately) obtained on two publicly available datasets(BUSI and OASUBD), prove the superiority of our method.
CONCLUSIONS: HAG-Net can better utilize multi-resolution features to localize the breast tumors. Demonstrating excellent generalizability and applicability for breast tumors segmentation compare to other state-of-the-art methods.
METHODS: The method is equipped with multi-scale guidance block (MSG) for guiding the extraction of low-resolution location information. Short multi-head self-attention (S-MHSA) and convolutional block attention module are used to capture global features and long-range dependencies. Finally, the segmentation results are obtained by fusing multi-scale contextual information.
RESULTS: We compare with 7 state-of-the-art methods on two publicly available datasets through five random fivefold cross-validations. The highest dice coefficient, Jaccard Index and detect rate ([Formula: see text]%, [Formula: see text]%, [Formula: see text]% and [Formula: see text]%, [Formula: see text]%, [Formula: see text]%, separately) obtained on two publicly available datasets(BUSI and OASUBD), prove the superiority of our method.
CONCLUSIONS: HAG-Net can better utilize multi-resolution features to localize the breast tumors. Demonstrating excellent generalizability and applicability for breast tumors segmentation compare to other state-of-the-art methods.
摘要：
目的：近年来，乳腺癌已经成为女性的最大威胁。有许多研究致力于乳腺肿瘤的精确分割，这在计算机辅助诊断中是不可缺少的。深度神经网络实现了图像的精确分割。然而,卷积层偏向于提取局部特征，随着网络的加深，往往会丢失全局和位置信息，这导致乳腺肿瘤分割精度下降。出于这个原因,我们提出了一种混合注意力引导网络(HAG-Net)。我们相信该方法将提高乳腺超声图像中肿瘤的检出率和分割率。
方法：该方法配备了多尺度制导块（MSG），用于指导低分辨率位置信息的提取。短多头自注意(S-MHSA)和卷积块注意模块用于捕获全局特征和远程依赖关系。最后,分割结果通过融合多尺度上下文信息得到。
结果：我们通过五个随机的五倍交叉验证，在两个公开可用的数据集上与7种最先进的方法进行了比较。骰子系数最高，Jaccard指数和检测率([公式：见正文]%，[公式:见文本]%,[公式:见文本]%和[公式:见文本]%,[公式:见文本]%,[公式:见文本]%,分别)在两个公开可用的数据集(BUSI和OASUBD)上获得，证明了我们方法的优越性。
结论：HAG-Net可以更好地利用多分辨率特征定位乳腺肿瘤。与其他最先进的方法相比，对乳腺肿瘤分割具有出色的通用性和适用性。
方法：该方法配备了多尺度制导块（MSG），用于指导低分辨率位置信息的提取。
结果：我们通过五个随机的五倍交叉验证，在两个公开可用的数据集上与7种最先进的方法进行了比较。
结论：HAG-Net可以更好地利用多分辨率特征定位乳腺肿瘤。
