Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification

Abstract Background Recently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model. Method In this work, there are two main goals: i) present a fully automated pre-processing algorithm for a smart patch selection within histopathological images, and ii) evaluate the impact of the proposed strategy within a deep learning framework for the detection of prostate and breast cancer. The proposed algorithm is specifically designed to extract patches only on informative regions (i.e., high density of nuclei), most likely representative of where cancer can be detected. Results Our strategy was developed and tested on 1000 hematoxylin and eosin (H&E) stained images of prostate and breast tissue. By combining a stain normalization step and a segmentation-driven patch extraction, the proposed approach is capable of increasing the performance of a computer-aided diagnosis (CAD) system for the detection of prostate cancer (18.61% accuracy improvement) and breast cancer (17.72% accuracy improvement). Conclusion We strongly believe that the integration of the proposed pre-processing steps within deep learning frameworks will allow the achievement of robust and reliable CAD systems. Being based on nuclei detection, this strategy can be easily extended to other glandular tissues (e.g., colon, thyroid, pancreas, etc.) or staining methods (e.g., PAS)

Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification

Background Recently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model. Method In this work, there are two main goals: i) present a fully automated pre-processing algorithm for a smart patch selection within histopathological images, and ii) evaluate the impact of the proposed strategy within a deep learning framework for the detection of prostate and breast cancer. The proposed algorithm is specifically designed to extract patches only on informative regions (i.e., high density of nuclei), most likely representative of where cancer can be detected. Results Our strategy was developed and tested on 1000 hematoxylin and eosin (H&E) stained images of prostate and breast tissue. By combining a stain normalization step and a segmentation-driven patch extraction, the proposed approach is capable of increasing the performance of a computer-aided diagnosis (CAD) system for the detection of prostate cancer (18.61% accuracy improvement) and breast cancer (17.72% accuracy improvement). Conclusion We strongly believe that the integration of the proposed pre-processing steps within deep learning frameworks will allow the achievement of robust and reliable CAD systems. Being based on nuclei detection, this strategy can be easily extended to other glandular tissues (e.g., colon, thyroid, pancreas, etc.) or staining methods (e.g., PAS)