Dataset of breast ultrasound images

By Walid Al-Dhabyani, M. Gomaa, H. Khaled, A. Fahmy. Data in Brief, 2019

2,260 citations recorded by the source corpus.

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of early deaths. The data presented in this article reviews the medical images of breast cancer using ultrasound scan. Breast Ultrasound Dataset is categorized into three classes: normal, benign, and malignant images. Breast ultrasound images can produce great results in classification, detection, and segmentation of breast cancer when combined with machine learning.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5c3e024698538385a7be01f1234eeac6a3e46186

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

UNeXt: MLP-based Rapid Medical Image Segmentation Network (2022, methodology): “Datasets: To make our experiments as close to point-of-care imaging as possible, we pick International Skin Imaging Collaboration (ISIC 2018) [8] and Breast UltraSound Images (BUSI) [4] datasets to benchmark our results.”
Transformers in Medical Image Analysis: A Review (2022, methodology): “[53] Breast Cancer Breast BUSI [54] Dataset B [55] apply ViT on classification of breast ultrasound images Jiang et al.”
MedViT: A Robust Vision Transformer for Generalized Medical Image Classification (2023, methodology): “BreastMNIST is based on a dataset [72] of 780 breast ultrasound images.”
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation (2024, methodology): “BUSI The BUSI dataset [1] is made up of ultrasound images depicting normal, benign, and malignant breast cancer cases along with their corresponding segmentation maps.”
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis (2020, methodology): “BreastMNIST [14] Breast Ultrasound Binary-Class (2) 546 78 156”
Segment Anything Model for medical image analysis: an experimental study (2023, neutral): “US-Breast Dataset of Breast Ultrasound Images [2] Ultrasound 1 Breast 780”
Segment Anything in Medical Images (2023, background): “…segmentation in various MR sequences (e.g., T1, T2, ADC, FLAIR) [24] [12], breast tumor, liver, and vessel segmentation in ultrasound images [2][48], heart and lungs segmentation in X-Ray images [40], polyp and instrument segmentation in endoscope images [3][16], vessel segmentation in…”
Federated learning for medical image analysis: A survey (2023, neutral): “[117] Breast tumor segmentation BUS [118], BUSIS [119], UDIAT [120] Ultrasound VII.”
Transformers in Medical Imaging: A Survey (2022, background): “Gheflati et al. [243] Breast Ultrasound 2D BUSY [244] Yap et al. [245] Accuracy AuC Pure First application of ViTs to ultrasound images classification.”
EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation (2024, methodology): “Based on these empirical observations, we choose [1 , 3 , 5] kernels in all our experiments.”