Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

By Haibo Wang, Angel Cruz-Roa, Ajay Basavanhally, Hannah Gilmore, Natalie N. C. Shih, M. Feldman, John Tomaszewski, Fabio A. González, Anant Madabhushi. Journal of Medical Imaging, 2014

334 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/b61f0ad7b185fa23ae37b4cdbfa738d1af01671f

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases (2016, methodology): “[49,50] The results of the blue ratio segmentation approach are dilated into a 20 disk radial mask [Figure 9b].”
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology (2016, background): “describing the texture, color, and spatial arrangement of nuclei and glands on digitized images of tissue slides [1], [2], [3], [4], [5], [6].”
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, background): “• Exclude papers focusing on detection (Cruz-Roa et al. 2017; Mina and Mat Isa 2015; Wang et al. 2014), segmentation (Chen et al. 2017a; Pan et al. 2017), cancer patient survivability (Jyh-Horng et al. 2014; Sivachitra and Vijayachitra 2015), or CAD analysis (Tan et al. 2013; van Zelst et al.…”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “handcrafted features and a CNN model for the detection of mitosis in breast cancer images is presented in [46].”
Deep neural network models for computational histopathology: A survey (2019, background): “Since training of CNN models is often complex and requires a larger training set, the earliest works (Wang et al., 2014; Kashif et al., 2016; Xing et al., 2016; Romo-Bucheli et al., 2016; Wang et al., 2016b) focused on integrating CNN with biologically interpretable handcrafted features.”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “Thus, in lieu of feature engineering, learning how to represent data and how to solve for the decision are now done in a completely data-driven fashion, notwithstanding the existence of approaches combining feature-engineering and deep learning [99].”
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning (2017, background): “However, an additional beneﬁt to deep learning is that if hand-designed features are thought to be useful, they can be included as well in case they improve performance (36–40).”
Image analysis and machine learning in digital pathology: Challenges and opportunities (2016, positive): “sought to address these challenges by presenting a convergent approach (Wang et al., 2014) to combine domain inspired features with deep learning features to detect mitoses.”
A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images (2016, methodology): “Handcrafted features were combined with CNN features in [26] for mitosis detection in breast cancer pathology.”
A deep learning model to predict RNA-Seq expression of tumours from whole slide images (2020, background): “(3) Improving predictive performances for different tasks, in a transfer learning framework, as shown here for a realistic setup, for microsatellite instability (MSI) status prediction from non-annotated WSIs.”
Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017, methodology): “…of digitized whole pathology slides and the concomitant increase in the number of publicly available large histopathology image databases, such as The Cancer Genome Atlas, has made digital pathology a good candidate for the application of deep learning based classification models 13,17,27–33 .”