DBSAM
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UTERUS: The uterus dataset (Huang et al. 2021) collected from the treatment device HIFU Pro2008 of Shenzhen ProHuiren Company. The dataset comprises 495 HIFU treatment ultrasound monitoring images of uterus, with 330 images randomly selected for training, 50 images for validating and 115 images for testing. The target region for treatment is the tumor region in the ultrasound image, as marked by professional doctors, and the image size is 448×544 pixels.BUSI: The breast ultrasound dataset consists of 780 images with pixel-wise breast cancer annotations (Al-Dhabyani et al. 2020). The dataset was collected from two different kinds of ultrasonic imaging devices, including LOGIQ E9 and LOGIQ E9 Agile. BUSI includes three classes of breast cases: 487 for benign, 210 for malignant, and 133 for normal. All images are preprocessed by (Al-Dhabyani et al. 2020). In the experiments in this paper, we used only benign and malignant, with a total of 647 images.BUSC: The Mendeley (Paulo Sergio Rodrigues, 2017) ultrasound dataset includes 100 benign images and 150 malignant cancer images. The original resolution of ultrasound images is 64×64 pixels, later transformed to 128×128 pixels. The dataset is basically classification-based, and no ground truth images are provided. Therefore, with the help of an experienced radiologist, benign and malignant tumor images are annotated for the model training process. BUS: The UDIAT dataset was collected at the UDIAT Diagnostic Centre of the Parc Tauli Corporation, Sabadell, Spain, using a Siemens ACUSON scanner. The dataset contains 163 US images: 109 benign and 54 malignant cases, with only one lesion per image.STU: The dataset, comprising 42 breast ultrasound images from the Hospital of Shantou University, is relatively small. Despite its limited size, it shares the same segmentation object as BUSI. Consequently, we designate this dataset as the unseen test set to assess the generalization ability of various models.DDTI: The thyroid nodule dataset contains 637 ultrasound images with pixel-wise thyroid nodule annotations (Pedraza et al. 2015). DDTI was collected from two different kinds of ultrasonic imaging devices, including TOSHIBA Nemio 30 and TOSHIBA Nemio MX. We adopt the data pre-processed by (Gong et al. 2023). TN3K: The dataset comprises 3493 ultrasound images meticulously annotated for thyroid nodules at Zhujiang Hospital, South Medical University. Following Gong et al.'s methodology, the TN3K dataset is partitioned into subsets of 2303, 576, and 614 images for training, validation, and testing, respectively. In this paper, we utilize only 614 test images as an unseen dataset to evaluate all the foundation models.
UTERUS（子宫）数据集：该数据集（Huang等，2021）采集自深圳普厚仁公司的HIFU Pro2008治疗设备。数据集包含495张子宫超声治疗监测图像，随机选取330张用于训练、50张用于验证、115张用于测试。治疗目标区域为超声图像中的肿瘤区域，由专业医师标注，图像分辨率为448×544像素。 BUSI（乳腺超声）数据集：共包含780张带像素级乳腺癌标注的超声图像（Al-Dhabyani等，2020）。该数据集采集自两种不同型号的超声成像设备：LOGIQ E9与LOGIQ E9 Agile。BUSI涵盖三类乳腺病例：良性487例、恶性210例、正常133例。所有图像已由Al-Dhabyani等（2020）完成预处理。本文实验中仅使用良性与恶性病例，共计647张图像。 BUSC（Mendeley超声）数据集：Mendeley数据集（Paulo Sergio Rodrigues，2017）包含100张良性肿瘤图像与150张恶性肿瘤图像。超声图像原始分辨率为64×64像素，后被调整至128×128像素。该数据集本质为分类任务数据集，未提供真值标注图像。因此，本研究在资深放射科医师协助下，对良、恶性肿瘤图像进行标注，以用于模型训练。 BUS（UDIAT）数据集：UDIAT数据集采集自西班牙萨瓦德尔Parc Tauli公司的UDIAT诊断中心，使用西门子ACUSON扫描仪完成。该数据集包含163张超声图像：良性109例、恶性54例，每张图像仅包含一处病灶。 STU数据集：该数据集由汕头大学医院提供的42张乳腺超声图像组成，规模相对有限。尽管样本量较小，但其分割目标与BUSI完全一致。据此，我们将该数据集划定为未见测试集，用于评估各类模型的泛化能力。 DDTI（甲状腺结节）数据集：包含637张带像素级甲状腺结节标注的超声图像（Pedraza等，2015）。DDTI采集自两种不同型号的超声成像设备：TOSHIBA Nemio 30与TOSHIBA Nemio MX。本研究采用Gong等（2023）预处理后的数据集。 TN3K数据集：该数据集包含南方医科大学珠江医院精心标注的3493张甲状腺结节超声图像。参照Gong等的方法，TN3K数据集被划分为训练集（2303张）、验证集（576张）与测试集（614张）。本文中，我们仅使用其中614张测试图像作为未见数据集，用于评估所有基础模型。
