Breast cancer histopathological image classification using Convolutional Neural Networks

By Fabio Alexandre Spanhol, Luiz Oliveira, Caroline Petitjean, Laurent Heutte. IEEE International Joint Conference on Neural Network, 2016

838 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3dbb621f1c35c659f3ce6efadad7aa16308fae13

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Federated learning for medical image analysis: A survey (2023, background): “(2023) [115] Breast tumor classification BreakHis [116] Pathology CNN Wicaksana et al.”
Breast Tumor Cellularity Assessment using Deep Neural Networks (2019, methodology): “As such, they have successfully been utilized for digital pathology image analysis and have demonstrated great potential for improving breast cancer diagnostics [38, 4, 5, 32].”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “[128] BreakHis 2016 725 BreakHis was composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X).”
Machine Learning Methods for Histopathological Image Analysis (2017, methodology): “Since the errors made by a machine learning system reportedly differ from those made by a human pathologist [15], classification accuracy could be improved using CAD system.”
Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, methodology): “AlexNet was used for the construction of CNN to classify the benign mitosis from the malignant one using the histopathological images [49].”
Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks (2018, methodology): “CNNs have been applied to address the task of breast cancer histology images classification such as in [22], where the authors divided the histology images into small batches and then used to train CNN.”
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018, background): “To our knowledge, the reported results are superior to the automated analysis of breast cancer images reported in literature [1,2,24].”
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;…”
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network (2018, methodology): “Most previous studies have reported classification results for benign and malignant cases [8, 15, 18].”
Deep features for breast cancer histopathological image classification (2017, methodology): “Compared with the CNN-based approach presented in [4], which achieved higher results, DeCAF features beat that method in the 200× zoom level, and in 400× in image accuracy.”
Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network (2017, methodology): “[13] proposed the use of DCNN to classify the breast cancer histopathological images, Li et al.”
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model (2017, methodology): “Although patch-based methods are common occurrence 17, 24, 25 ; however, it brings up an obvious disadvantage that pathologists have to make biomarkers for the cancerous region because the region of cancerization is only a fraction of breast cancer histopathological images.”