scholarly journals 3D Deep Learning on Medical Images: A Review

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5097 ◽  
Author(s):  
Satya P. Singh ◽  
Lipo Wang ◽  
Sukrit Gupta ◽  
Haveesh Goli ◽  
Parasuraman Padmanabhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Imaging Data ◽  
Learning Models ◽  
Processing Technologies ◽  
3D Cnn

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

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.

scholarly journals Application of Artificial Intelligence in Cardiovascular Imaging

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Panjiang Ma ◽  
Qiang Li ◽  
Jianbin Li
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Cardiovascular Imaging ◽  
Development Trend ◽  
Imaging Data ◽  
Label Information

During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems in various industries has continued to expand, resulting in a huge increase in industry data. As for the medical industry, huge unstructured data has been accumulated, so exploring how to use medical image data more effectively to efficiently complete diagnosis has an important practical impact. For a long time, China has been striving to promote the process of medical informatization, and the combination of big data and artificial intelligence and other advanced technologies in the medical field has become a hot industry and a new development trend. This paper focuses on cardiovascular diseases and uses relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. We have tried to achieve a complete diagnosis of cardiovascular medical imaging and localize the vulnerable lesion area. (1) We tested the classical object based on a convolutional neural network and experiment, explored the region segmentation algorithm, and showed its application scenarios in the field of medical imaging. (2) According to the data and task characteristics, we built a network model containing classification nodes and regression nodes. After the multitask joint drill, the effect of diagnosis and detection was also enhanced. In this paper, a weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced. (3) In the actual medical process, many medical images have the label information of high-level categories but lack the label information of low-level lesions. The proposed system exposes the possibility of lesion localization under weakly supervised conditions by taking cardiovascular imaging data to resolve these issues. Experimental results have verified that the proposed deep learning-enabled model has the capacity to resolve the aforementioned issues with minimum possible changes in the underlined infrastructure.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

scholarly journals Deep Convolution Neural Networks for Medical Image Analysis

2018 ◽  
Vol 7 (3.33) ◽  
pp. 115 ◽  
Author(s):  
Myung Jae Lim ◽  
Da Eun Kim ◽  
Dong Kun Chung ◽  
Hoon Lim ◽  
Young Man Kwon
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Detection ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Histopathological Image ◽  
Approach Method

Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visually. And among the deep learning algorithms, the CNN(Convolutional Neural Network) specialized for the image was used to perform comparative analysis on whether it is breast cancer or not. Among the CNN algorithms, VGG16 and InceptionV3 were used, and transfer learning was used for the effective application of these algorithms.The data used in this paper is breast cancer histopathological image dataset classifying the benign and malignant of BreakHis. In the 2-class classification task, InceptionV3 achieved 98% accuracy. It is expected that this deep learning approach method will support the development of disease diagnosis through medical images.  

scholarly journals Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyunkwang Lee ◽  
Chao Huang ◽  
Sehyo Yune ◽  
Shahein H. Tajmir ◽  
Myeongchan Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Image Space ◽  
Sensor Data ◽  
Reconstruction Process ◽  
Partial Representation

Abstract Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.

Deep Learning Applications in Medical Imaging

2021 ◽  
pp. 178-208
Author(s):  
S. Sasikala ◽  
S. J. Subhashini ◽  
P. Alli ◽  
J. Jane Rubel Angelina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Learning Model ◽  
Learning Models ◽  
Driving System ◽  
Machine Learning Model ◽  
Car Driving ◽  
Machine Learning Models

Machine learning is a technique of parsing data, learning from that data, and then applying what has been learned to make informed decisions. Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way, but it has different capabilities. The main difference between deep and machine learning is, machine learning models become well progressively, but the model still needs some guidance. If a machine learning model returns an inaccurate prediction, then the programmer needs to fix that problem explicitly, but in the case of deep learning, the model does it by itself. Automatic car driving system is a good example of deep learning. On other hand, Artificial Intelligence is a different thing from machine learning and deep learning. Deep learning and machine learning both are the subsets of AI.

scholarly journals The ANTsX ecosystem for quantitative biological and medical imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas J. Tustison ◽  
Philip A. Cook ◽  
Andrew J. Holbrook ◽  
Hans J. Johnson ◽  
John Muschelli ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Open Source Software ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Network Architectures ◽  
Imaging Data ◽  
Software Libraries ◽  
Learning Capabilities

AbstractThe Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

scholarly journals Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1590
Author(s):  
Laith Alzubaidi ◽  
Muthana Al-Amidie ◽  
Ahmed Al-Asadi ◽  
Amjad J. Humaidi ◽  
Omran Al-Shamma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Transfer Learning ◽  
Medical Image ◽  
Medical Images ◽  
Image Data ◽  
Learning Approach ◽  
Learning Models ◽  
Annotation Process ◽  
Deep Learning Model

Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes—either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

scholarly journals Medical Imaging using Deep Learning Models

2021 ◽  
Vol 6 (5) ◽  
pp. 156-167
Author(s):  
Chetanpal Singh
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Potential Role ◽  
Medical Image Analysis ◽  
Learning Models ◽  
Quality Healthcare ◽  
Learning Techniques

Deep learning has played a potential role in quality healthcare with fast automated and proper medical image analysis. In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's body. However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image analysis. It is necessary to state here that the deep learning approach is gaining attention in the medical imaging field in evaluating the presence or absence of disease in a patient. Mammography images, digital histopathology images, computerized tomography, etc. are some of the areas on which DL implementation focuses. One upon going through the paper will get to know the recent development that has occurred in this field and come up with a critical review on this aspect. The paper has demonstrated in detail modern deep learning models that are implemented in medical image analysis. There is no doubt about the promising future of the deep learning models and according to experts; the implementation of deep learning techniques has outperformed medical experts in numerous tasks. However, deep learning also has some drawbacks and challenges that are required to be addressed like limited datasets and many more. To mitigate such kinds of challenges, researchers are working on this aspect so that they can enhance healthcare by deploying AI.

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