A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

By Angel Cruz-Roa, John Arevalo, Anant Madabhushi, Fabio A. González. 2013

389 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition, Oncology, Medical & Health AI

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Citations in the corpus

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning (2017, background): “Deep learning has also been used as a component in more complex systems that are able to play games [32, 40, 55, 58] or diagnose and classify diseases [15, 17, 25].”
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features (2017, neutral): “Various replacement approaches are studied in [20–23].”
Real-world data: a brief review of the methods, applications, challenges and opportunities (2022, background): “Promising and encouraging research work on this topic has already started [106–111], but more research is warranted.”
Efficient Training of Convolutional Deep Belief Networks in the Frequency Domain for Application to High-Resolution 2D and 3D Images (2015, background): “However, due to the high computational costs of training large networks, applications have been mostly limited to the analysis of 2D images (Ciresan et al., 2012; Cruz-Roa et al., 2013; Lee, Laine et al., 2011), or small 3D images, frequently extracted as patches of images that are too large to trai”
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images (2015, positive): “For instance, in [34], the authors employed a convolutional autoencoder neural network architecture (CNN) with autoencoder for histopathological image representation learning.”
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images (2016, result): “Cruz et al. [26] showed that for detection of basal-cell carcinoma, features learned using deep learning approaches produce superior and stable results compared to pre-deﬁned bag of feature and canonical (discrete cosine transform/wavelet transform based) representations.”
Privacy-preserving deep learning (2015, background): “Deep learning has shown promise for analyzing complex biomedical data related to cancer [13, 22, 32] and genetics [15, 56].”
Breast cancer histopathological image classification using Convolutional Neural Networks (2016, background): “As a particular deep learning technique, Convolutional Neural Networks (CNNs) [13] have achieved success in image classification problems, including medical image analysis [20], [21], [22], [23].”
Lung Nodule Classification Using Deep Features in CT Images (2015, methodology): “…in the overall accuracy over the earlier methods by using deep learning architecture for early diagnosis of alzheimer’s disease; CruzRoa et al. [9] reported an improvement of around 7% over canonical representations while using a deep learning architecture for image representation and automatic…”