18,196 research outputs found
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
A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders
Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more
challenging tasks that oncology medicine deals with. With the main aim
of fitting the more appropriate treatments, current personalized medicine
focuses on using data from heterogeneous sources to estimate the evolu-
tion of a given disease for the particular case of a certain patient. In recent
years, next-generation sequencing data have boosted cancer prediction by
supplying gene-expression information that has allowed diverse machine
learning algorithms to supply valuable solutions to the problem of cancer
subtype classification, which has surely contributed to better estimation
of patient’s response to diverse treatments. However, the efficacy of these
models is seriously affected by the existing imbalance between the high
dimensionality of the gene expression feature sets and the number of sam-
ples available for a particular cancer type. To counteract what is known
as the curse of dimensionality, feature selection and extraction methods
have been traditionally applied to reduce the number of input variables
present in gene expression datasets. Although these techniques work by
scaling down the input feature space, the prediction performance of tradi-
tional machine learning pipelines using these feature reduction strategies
remains moderate. In this work, we propose the use of the Pan-Cancer
dataset to pre-train deep autoencoder architectures on a subset com-
posed of thousands of gene expression samples of very diverse tumor
types. The resulting architectures are subsequently fine-tuned on a col-
lection of specific breast cancer samples. This transfer-learning approach
aims at combining supervised and unsupervised deep learning models
with traditional machine learning classification algorithms to tackle the
problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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