Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China; School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China.
Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China.
Comput Biol Med. 2023 Sep;164:107255. doi: 10.1016/j.compbiomed.2023.107255. Epub 2023 Jul 10.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown high sensitivity to diagnose breast cancer. However, few computer-aided algorithms focus on employing DCE-MR images for breast cancer diagnosis due to the lack of publicly available DCE-MRI datasets. To address this issue, our work releases a new DCE-MRI dataset called BreastDM for breast tumor segmentation and classification. In particular, a dataset of 232 patients selected with DCE-MR images for benign and malignant cases is established. Each case consists of three types of sequences: pre-contrast, post-contrast, and subtraction sequences. To show the difficulty of breast DCE-MRI tumor image segmentation and classification tasks, benchmarks are achieved by state-of-the-art image segmentation and classification algorithms, including conventional hand-crafted based methods and recently-emerged deep learning-based methods. More importantly, a local-global cross attention fusion network (LG-CAFN) is proposed to further improve the performance of breast tumor images classification. Specifically, LG-CAFN achieved the highest accuracy (88.20%, 83.93%) and AUC value (0.9154,0.8826) in both groups of experiments. Extensive experiments are conducted to present strong baselines based on various typical image segmentation and classification algorithms. Experiment results also demonstrate the superiority of the proposed LG-CAFN to other breast tumor images classification methods. The related dataset and evaluation codes are publicly available at smallboy-code/Breast-cancer-dataset.
动态对比增强磁共振成像（DCE-MRI）已被证明对诊断乳腺癌具有很高的敏感性。然而，由于缺乏公开可用的 DCE-MRI 数据集，很少有计算机辅助算法专注于使用 DCE-MR 图像进行乳腺癌诊断。为了解决这个问题，我们的工作发布了一个新的 DCE-MRI 数据集，称为 BreastDM，用于乳腺癌肿瘤的分割和分类。特别是，建立了一个由 232 名患者的 DCE-MR 图像选择的良性和恶性病例组成的数据集。每个病例包括三种类型的序列：对比前、对比后和减影序列。为了展示乳腺 DCE-MRI 肿瘤图像分割和分类任务的难度，通过最先进的图像分割和分类算法实现了基准，包括传统的基于手工制作的方法和最近出现的基于深度学习的方法。更重要的是，提出了一种局部-全局交叉注意力融合网络（LG-CAFN），以进一步提高乳腺肿瘤图像分类的性能。具体来说，LG-CAFN 在两组实验中都实现了最高的准确性（88.20%，83.93%）和 AUC 值（0.9154，0.8826）。进行了广泛的实验，提出了基于各种典型图像分割和分类算法的强大基线。实验结果还表明，所提出的 LG-CAFN 优于其他乳腺肿瘤图像分类方法。相关的数据集和评估代码可在 smallboy-code/Breast-cancer-dataset 上公开获取。
