Dataset of breast ultrasound images.
TLDR
The data presented in this article reviews the medical images of breast cancer using ultrasound scan using Breast Ultrasound Dataset, which is categorized into three classes: normal, benign, and malignant images.About:
This article is published in Data in Brief.The article was published on 2020-02-01 and is currently open access. It has received 501 citations till now. The article focuses on the topics: Breast ultrasound & Breast cancer.read more
Citations
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Journal ArticleDOI
Computer?aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks
TL;DR: Results indicated different image content representations that affect the prediction performance of the CAD system, more image information improves the predictions performance, and the tumor shape feature can improve the diagnostic effect.
Proceedings ArticleDOI
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
TL;DR: MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets, and has compared several baseline methods, including open-source or commercial AutoML tools.
Posted ContentDOI
MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification
Jiancheng Yang,Rui Shi,Donglai Wei,Zequan Liu,Lin Zhao,Bilian Ke,Hanspeter Pfister,Bingbing Ni +7 more
TL;DR: A large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D, and benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools.
Journal ArticleDOI
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network.
Michal Byra,Michal Byra,Piotr Jarosik,Aleksandra Szubert,Michael Galperin,Haydee Ojeda-Fournier,Linda K. Olson,Mary O'Boyle,Christopher Comstock,Michael P. Andre +9 more
TL;DR: A selective kernel (SK) U-Net convolutional neural network to adjust network’s receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions for breast mass segmentation in ultrasound (US).
Journal ArticleDOI
Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion
Kiran Jabeen,Muhammad Attique Khan,Majed Alhaisoni,Usman Tariq,Yudong Zhang,Ameer Hamza,Arturas Mickus,Robertas Damaševičius +7 more
TL;DR: A new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features is proposed, which outperforms recent techniques.
References
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Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images
TL;DR: An overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets is confirmed.