An overview of deep learning in medical imaging

Anaya-Isaza, Andrés; Mera-Jiménez, Leonel; Zequera-Diaz, Martha

doi:10.1016/j.imu.2021.100723

Cited by 70 publications

(32 citation statements)

References 94 publications

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“…Some of the drawbacks of the AI technology have been surveyed in [55][56][57][58]. Additionally, the current challenges of deep learning technologies in the context of medical image analysis have been reviewed and defined in [10]. That article reviewed the basic concepts of deep learning and the most recent advances in medical images.…”

Section: Related Workmentioning

confidence: 99%

“…These solutions can be divided into two types: classical and deep learning models. Although the deep learning approach has achieved exceptional performance in the context of medical image analysis, several problems remain, as discussed in [10]. Over-fitting, the high complexity of deploying deep learning models, and the loss of some visual information owing to the preprocessing phase are some of these concerns.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Alhussan

Ghoneim

Atteia

et al. 2022

Sensors

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One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Alhussan

Ghoneim

Atteia

et al. 2022

Sensors

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“…The deep learning field has been widely used in image processing and classification ( Huang et al, 2017a ), the medical field ( Anaya-Isaza, Mera-Jiménez & Zequera-Diaz, 2021 ; Kikkisetti et al, 2020 ), speech recognition ( Nurvitadhi et al, 2017 ), and natural language processing and translations ( Amodei et al, 2016 ; Egger et al, 2021 ). With the rapid growth in data and model size, there is a need for better and robust hardware and software resources like the packages and most advanced libraries for data processing and the faster training of complex models.…”

Section: Introductionmentioning

confidence: 99%

Effect of neural network structure in accelerating performance and accuracy of a convolutional neural network with GPU/TPU for image analytics

Ravikumar

Sriraman

Saketh

et al. 2022

PeerJ Computer Science

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Background In deep learning the most significant breakthrough in the field of image recognition, object detection language processing was done by Convolutional Neural Network (CNN). Rapid growth in data and neural networks the performance of the DNN algorithms depends on the computation power and the storage capacity of the devices. Methods In this paper, the convolutional neural network used for various image applications was studied and its acceleration in the various platforms like CPU, GPU, TPU was done. The neural network structure and the computing power and characteristics of the GPU, TPU was analyzed and summarized, the effect of these on accelerating the tasks is also explained. Cross-platform comparison of the CNN was done using three image applications the face mask detection (object detection/Computer Vision), Virus Detection in Plants (Image Classification: agriculture sector), and Pneumonia detection from X-ray Images (Image Classification/medical field). Results The CNN implementation was done and a comprehensive comparison was done on the platforms to identify the performance, throughput, bottlenecks, and training time. The CNN layer-wise execution in GPU and TPU is explained with layer-wise analysis. The impact of the fully connected layer and convolutional layer on the network is analyzed. The challenges faced during the acceleration process were discussed and future works are identified.

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“…In the last decade, medical researchers have started to extensively rely on machine learning (ML) and artificial neural networks (ANNs) to gain further insights into large amounts of complex and intertwined data (Anaya-Isaza et al, 2021 ; Allegra et al, 2022 ). Records concerning patients' clinical and genetic features, pathologies, interventions, hospitalizations, and follow ups are deeply investigated through survival analysis models, whose goal is to provide ad hoc treatment options and ultimately shed light on the origins of the disease (Wu et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

An energy-efficient in-memory computing architecture for survival data analysis based on resistive switching memories

et al. 2022

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One of the objectives fostered in medical science is the so-called precision medicine, which requires the analysis of a large amount of survival data from patients to deeply understand treatment options. Tools like machine learning (ML) and deep neural networks are becoming a de-facto standard. Nowadays, computing facilities based on the Von Neumann architecture are devoted to these tasks, yet rapidly hitting a bottleneck in performance and energy efficiency. The in-memory computing (IMC) architecture emerged as a revolutionary approach to overcome that issue. In this work, we propose an IMC architecture based on resistive switching memory (RRAM) crossbar arrays to provide a convenient primitive for matrix-vector multiplication in a single computational step. This opens massive performance improvement in the acceleration of a neural network that is frequently used in survival analysis of biomedical records, namely the DeepSurv. We explored how the synaptic weights mapping strategy and the programming algorithms developed to counter RRAM non-idealities expose a performance/energy trade-off. Finally, we discussed how this application is tailored for the IMC architecture rather than being executed on commodity systems.

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An overview of deep learning in medical imaging

Cited by 70 publications

References 94 publications

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Effect of neural network structure in accelerating performance and accuracy of a convolutional neural network with GPU/TPU for image analytics

An energy-efficient in-memory computing architecture for survival data analysis based on resistive switching memories

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