Medical Image Analysis using Convolutional Neural Networks: A Review

Anwar, Syed Muhammad; Majid, Muhammad; Qayyum, Adnan; Awais, Muhammad; Alnowami, Majdi R.; Khan, Muhammad Khurram

doi:10.1007/s10916-018-1088-1

Cited by 943 publications

(576 citation statements)

References 90 publications

Supporting

Mentioning

477

Contrasting

Unclassified

Order By: Relevance

“…Medical imaging refers to the processes that provide visual information of the human body with the purpose of aiding physicians to make diagnostics and treatments more efficient [115]. The most widespread applications of DL that used medical data are just the medical image processing applications, and it is due mainly to the success of this approach in computer vision [48,51] and the success of CNN architecture for image analysis.…”

Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

View full text Add to dashboard Cite

In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

show abstract

Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

View full text Add to dashboard Cite

show abstract

“…DNNs are versatile tools for various applications, among which image analysis for general purpose feature recognition as well as for optical microscopy are prominent. (22)(23)(24)(25)(26) Recently, the U-net architecture has been demonstrated to be well suited for image segmentation. (27,28) Fundamentally, image segmentation is similar to sBG estimation: A featurethe PSF without BGis overlaid with the sBG, which should be identified from the combined image in order to subsequently remove it.…”

Section: Resultsmentioning

confidence: 99%

Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet

Möckl

Roy

Petrov

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3D localization microscopy or single-molecule tracking. Here, we introduce BGnet, a deep neural network with a U-net-type architecture, as a general method to rapidly estimate the background underlying the image of a point source with excellent accuracy, even when point spread function (PSF) engineering is in use to create complex PSF shapes. We trained BGnet to extract the background from images of various PSFs and show that the identification is accurate for a wide range of different interfering background structures constructed from many spatial frequencies. Furthermore, we demonstrate that the obtained background-corrected PSF images, both for simulated and experimental data, lead to a substantial improvement in localization precision. Finally, we verify that structured background estimation with BGnet results in higher quality of super-resolution reconstructions of biological structures. Significance:A main factor that degrades image quality in fluorescence microscopy is unwanted background fluorescence. Background is almost never uniform, especially in complex samples. Rather, background usually exhibits some structure, making it very difficult to distinguish from the signal of interest. Due to this challenging problem, background fluorescence is often assumed to be uniform even though it is not. This assumption leads to deteriorated image quality, e.g. in localization-based superresolution microscopy, and to the introduction of uncharacterized biases. To overcome this challenge, we developed a general framework rooted in deep learning to accurately and rapidly estimate arbitrarily structured background using several distinct image shapes for the single emitter. Proper background estimation allows for critical performance improvement of various optical microscopy methods. Main Text:

show abstract

“…Recently, the most widely used machine learning techniques are based on deep learning, where various functions are used to transform the data into a hierarchical representation [47]. DL has gained wide attention in image categorization, image recognition, speech recognition and natural language processing, and medical image analysis [8] [56]. One major advantage of DL is the fact that features are extracted directly from raw data allowing feature learning [27].…”

Section: Radiomics Using Deep Learningmentioning

confidence: 99%

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

Anwar

Altaf

Rafique

et al. 2020

Radiomics and Radiogenomics in Neuro-Oncology

Self Cite

View full text Add to dashboard Cite

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistance in diagnosis of cancer, planning of treatment strategy, and prediction of survival. Radiomics in neuro-oncology has progressed significantly in the recent past. Deep learning has outperformed conventional machine learning methods in most image-based applications. Convolutional neural networks (CNNs) have seen some popularity in radiomics, since they do not require hand-crafted features and can automatically extract features during the learning process. In this regard, it is observed that CNN based radiomics could provide state-of-the-art results in neuro-oncology, similar to the recent success of such methods in a wide spectrum of medical image analysis applications. Herein we present a review of the most recent best practices and establish the future trends for AI enabled radiomics in neuro-oncology.

show abstract

Medical Image Analysis using Convolutional Neural Networks: A Review

Cited by 943 publications

References 90 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

Contact Info

Product

Resources

About