6,999 research outputs found
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
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
Automated segmentation of tissue images for computerized IHC analysis
This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie
A Comparative Study on the Methods Used for the Detection of Breast Cancer
Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques
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 Andalucía Tech
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