A Dataset for Breast Cancer Histopathological Image Classification

By Fabio Alexandre Spanhol, Luiz Oliveira, Caroline Petitjean, Laurent Heutte. 2016

1,610 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mengjie Zhang
2. Bing Xue
3. Yanan Sun
4. Gary G. Yen
5. Yaochu Jin
6. Handing Wang
7. Max Welling
8. Taco Cohen

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Mengjie Zhang
3. Max Welling
4. Dacheng Tao
5. Xiaogang Wang
6. Bing Xue
7. Ming-Hsuan Yang
8. Yanan Sun

In Evaluation & Benchmarks

1. Caroline Petitjean
2. Luiz Oliveira
3. Laurent Heutte
4. Fabio Alexandre Spanhol
5. Mohammed Ali Al-Garadi
6. Xin Gao
7. Bing Xue
8. Xiaojun Chang

Citations in the corpus

• Rotation Equivariant CNNs for Digital Pathology (2018, methodology): “BreakHis As an additional evaluation method, we assess the performance of the proposed model on the binary classification task of BreakHis as described in Section 3.1.”
• Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, methodology): “architecture design algorithms to solve some specific tasks, such as recognizing breast cancer on the BreakHis dataset [66].”
• Breast cancer histopathological image classification using Convolutional Neural Networks (2016, methodology): “[11] introduced a dataset composed of 7,909 breast histopathological images acquired on 82 patients.”
• Deep neural network models for computational histopathology: A survey (2019, background): “BreakHis 2016 (Spanhol et al., 2015) Breast Breast cancer detection 82 (7909 patches) WSL benign vs.”
• A Survey of Deep Active Learning (2020, background): “[30, 55, 66, 186] [123, 187, 206] [36, 181, 205] Erie County [66], EEG [9], BreaKHis [210], SVEB, SVDB [186] Biomedical”
• SupportNet: solving catastrophic forgetting in class incremental learning with support data (2018, positive): “The Breast Cancer Histopathological Database (Break His) 3 (Spanhol et al. , 2016) is composed of 9,109 microscopic images of the breast tumor tissue, which were collected from 82 patients with four different magnifying factors (40X, 100X, 200X, 400X).”
• Deep learning for magnification independent breast cancer histopathology image classification (2016, methodology): “On the other hand, for classification purposes, hand-crafted features are often employed [5], [21], [2], [7].”
• Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “…by using three strategies, namely, random number of patches (Spanhol et al. 2016a, 2017), patches with 50% overlapping (Araujo et al. 2017; Spanhol et al. 2016a), and patches with no overlapping (fixed size window) (Cheng et al. 2016; Duraisamy and Emperumal 2017; Feng et al. 2018;…”
• Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks (2018, methodology): “MATERIALS The data set used in this work is BreaKHis [14], which comprises microscopic images of biopsy for benign and malignant breast tumors with a total number of 7909 images.”
• Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model (2017, methodology): “12 used a breast cancer histopathological images dataset (BreaKHis), then provided a baseline of binary classification recognition rates by means of different feature descriptors and different traditional machine learning classifiers, the range of the accuracy is 80% to 85%.”