Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

By Dan Claudiu Ciresan, Alessandro Giusti, Luca Maria Gambardella, J. Urgen Schmidhuber. 2013

1,574 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (2013, neutral): “Figure 3 compares R-CNN to the entries in the ILSVRC 2013 competition and to the post-competition OverFeat result [34].”
Rotation Equivariant CNNs for Digital Pathology (2018, background): “Orthogonally, [1,10] propose a test-time augmentation strategy that averages the predictions of 90◦-rotated and mirrored versions to improve robustness to orientation-induced instability.”
Deep learning in neural networks: An overview (2014, methodology): “an object-detecting GPU-MPCNN ensemble (Ciresan et al., 2013).”
Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Several studies have used deep learning methods to better categorize breast cancer patients: denoising autoencoders, an unsupervised approach, can be used to cluster breast cancer patients [24], and convolutional neural networks (CNNs) can help count mitotic divisions, a feature that is highly corre”
Deep Networks with Internal Selective Attention through Feedback Connections (2014, background): “Deep convolutional neural networks (CNNs) [1] with max-pooling layers [2] trained by backprop [3] on GPUs [4] have become the state-of-the-art in object recognition [5, 6, 7, 8], segmentation/detection [9, 10], and scene parsing [11, 12] (for an extensive review see [13]).”
A survey on deep learning in medical image analysis (2017, methodology): “Consequently, AlexNet or other simple models such as GG are still popular for medical data, though recent landmark tudies all use a version of GoogleNet called Inception v3 ( Gulshan t al., 2016; Esteva et al., 2017; Liu et al., 2017 ).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, background): “, MNIST (2011), won numerous competitions [247, 38, 41, 39, 161, 42, 36, 134, 322, 37, 245], and achieved the first human-competitive or even superhuman results on well-known benchmarks, e.”
Demystifying Parallel and Distributed Deep Learning (2018, background): “Deep learning has been successfully implemented for a plethora of fields, ranging from image classification [110], through speech recognition [6] and medical diagnosis [44], to autonomous driving [22] and defeating human players in complex games [223].”
DeepTox: Toxicity Prediction using Deep Learning (2016, background): “…et al., 2012a; Krizhevsky et al., 2012), speech recognition (Dahl et al., 2012; Deng et al., 2013; Graves et al., 2013), text understanding and natural language processing (Socher and Manning, 2013; Sutskever et al., 2014), physics (Baldi et al., 2014), and life sciences (Cire¸san et al., 2013).”
Detecting Cancer Metastases on Gigapixel Pathology Images (2017, methodology): “By contrast, we use a non-maxima suppression method similar to [6] that repeats two steps until no values in the heatmap remain above a threshold t: (1) report the maximum and corresponding coordinate, and (2) set all values within a radius r of the maximum to 0.”
Deep learning for computational biology (2016, neutral): “Ciresan et al (2013) used five convolutional and pooling layers, followed by two fully connected layers, to find mitosis in breast histology images.”
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems (2019, neutral): “Driven by their current success on natural images, DNNs have become a popular tool for medical image processing tasks, such as cancer diagnosis (Cireşan et al., 2013; Esteva et al., 2017), diabetic retinopathy detection (Kaggle, 2015) and organ/landmark localization (Roth et al.”
Assessment of algorithms for mitosis detection in breast cancer histopathology images (2014, methodology): “A similar technique won the MITOS mitosis detection competition (Ciresan et al., 2013) and recently produced outstanding results in image classification (Ciresan et al.”
Deep Learning and Convolutional Neural Networks for Medical Image Computing (2017, methodology): “More complex CNN models [27, 50] have only about 150 or 15K parameters.”
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis (2014, positive): “, 1998), which has been successfully applied to medical imaging analysis (Ciresan et al., 2013; Hjelm et al., 2014; Liao et al., 2013; Shin et al., 2013; Suk and Shen, 2013).”