An Overview of Biomedical Image Analysis From the Deep Learning Perspective

2020 ◽  
pp. 197-218
Author(s):  
Shouvik Chakraborty ◽  
Kalyani Mali
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Biomedical Image Analysis ◽  
Comprehensive Overview ◽  
Biomedical Image ◽  
Research Areas ◽  
Analysis Methods ◽  
Computer Aided ◽  
Recent Developments ◽  
Active Research

Biomedical image analysis methods are gradually shifting towards computer-aided solutions from manual investigations to save time and improve the quality of the diagnosis. Deep learning-assisted biomedical image analysis is one of the major and active research areas. Several researchers are working in this domain because deep learning-assisted computer-aided diagnostic solutions are well known for their efficiency. In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented. This chapter can be helpful for the researchers to understand the recent developments and drawbacks of the present systems. The discussion is made from the perspective of the computer vision, pattern recognition, and artificial intelligence. This chapter can help to get future research directions to exploit the blessings of deep learning techniques for biomedical image analysis.

Deep Learning Techniques for Biomedical Image Analysis in Healthcare

2020 ◽  
pp. 31-46
Author(s):  
Sivakami A. ◽  
Balamurugan K. S. ◽  
Bagyalakshmi Shanmugam ◽  
Sudhagar Pitchaimuthu
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Image Interpretation ◽  
Medical Applications ◽  
Biomedical Image Analysis ◽  
Future Directions ◽  
Biomedical Image ◽  
Learning Techniques ◽  
Visual Problems

Biomedical image analysis is very relevant to public health and welfare. Deep learning is quickly growing and has shown enhanced performance in medical applications. It has also been widely extended in academia and industry. The utilization of various deep learning methods on medical imaging endeavours to create systems that can help in the identification of disease and the automation of interpreting biomedical images to help treatment planning. New advancements in machine learning are primarily about deep learning employed for identifying, classifying, and quantifying patterns in images in the medical field. Deep learning, a more precise convolutional neural network has given excellent performance over machine learning in solving visual problems. This chapter summarizes a review of different deep learning techniques used and how they are applied in medical image interpretation and future directions.

A Generalized Overview of the Biomedical Image Processing From the Big Data Perspective

2020 ◽  
pp. 133-158
Author(s):  
Mousomi Roy
Keyword(s):  
Image Analysis ◽  
Big Data ◽  
Data Analysis ◽  
Biomedical Data ◽  
Biomedical Image Analysis ◽  
Diagnostic Systems ◽  
Biomedical Image Processing ◽  
Domain Experts ◽  
Biomedical Image ◽  
Computer Aided

Computer-aided biomedical data and image analysis is one of the inevitable parts for today's world. A huge dependency can be observed on the computer-aided diagnostic systems to detect and diagnose a disease accurately and within the stipulated amount of time. Big data analysis strategies involve several advanced methods to process big data, such as biomedical images, efficiently and fast. In this work biomedical image analysis techniques from the perception of the big data analytics are studied. Big data and machine learning-based biomedical image analysis is helpful to achieve high accuracy results by maintaining the time constraints. It is also helpful in telemedicine and remote diagnostics where the physical distance of the patient and the domain experts is not a problem. This work can also be helpful in future developments in this domain and also helpful in improving present techniques for biomedical data analysis.

Deep learning in biomedical image analysis

2020 ◽  
pp. 239-263 ◽  
Author(s):  
Minjeong Kim ◽  
Chenggang Yan ◽  
Defu Yang ◽  
Qian Wang ◽  
Junbo Ma ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Biomedical Image Analysis ◽  
Biomedical Image

Deep Learning Models for Biomedical Image Analysis

2019 ◽  
pp. 128-148
Author(s):  
Bo Ji ◽  
Wenlu Zhang ◽  
Rongjian Li ◽  
Hao Ji
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Gene Expression Pattern ◽  
Disease Diagnosis ◽  
Biomedical Image Analysis ◽  
Imaging Data ◽  
Learning Models ◽  
Biomedical Image ◽  
Deep Feature Extraction

Biomedical image analysis has become critically important to the public health and welfare. However, analyzing biomedical images is time-consuming and labor-intensive, and has long been performed manually by highly trained human experts. As a result, there has been an increasing interest in applying machine learning to automate biomedical image analysis. Recent progress in deep learning research has catalyzed the development of machine learning in learning discriminative features from data with minimum human intervention. Many deep learning models have been designed and achieved superior performance in various data analysis applications. This chapter starts with the basic of deep learning models and some practical strategies for handling biomedical image applications with limited data. After that, case studies of deep feature extraction for gene expression pattern image annotations, imaging data completion for brain disease diagnosis, and segmentation of infant brain tissue images are discussed to demonstrate the effectiveness of deep learning in biomedical image analysis.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals A Feature Set for Cytometry on Digitized Microscopic Images

2003 ◽  
Vol 25 (1) ◽  
pp. 1-36 ◽  
Author(s):  
Karsten Rodenacker ◽  
Ewert Bengtsson
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Systematic Approach ◽  
Structural Features ◽  
Biomedical Image Analysis ◽  
Feature Sets ◽  
Biomedical Image ◽  
Rules Of Thumb ◽  
Quantitative Cytology ◽  
The Eu

Feature extraction is a crucial step in most cytometry studies. In this paper a systematic approach to feature extraction is presented. The feature sets that have been developed and used for quantitative cytology at the Laboratory for Biomedical Image Analysis of the GSF as well as at the Center for Image Analysis in Uppsala over the last 25 years are described and illustrated. The feature sets described are divided into morphometric, densitometric, textural and structural features. The latter group is used to describe the eu‐ and hetero‐chromatin in a way complementing the textural methods. The main goal of the paper is to bring attention to the need of a common and well defined description of features used in cyto‐ and histometrical studies. The application of the sets of features is shown in an overview of projects from different fields. Finally some rules of thumb for the design of studies in this field are proposed. Colour figures can be viewed onhttp://www.esacp.org/acp/2003/25‐1/rodenacker.htm.

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