A survey of deep learning models in medical therapeutic areas

Nogales, Alberto; García‐Tejedor, Álvaro J.; Martín, Diana Monge; Vara, Juan Serrano; Antón, Cristina

doi:10.1016/j.artmed.2021.102020

Cited by 36 publications

(24 citation statements)

References 80 publications

(53 reference statements)

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“…We will exemplify in Table 1 [2] applications in medicine and the performance of DL models depending on types of medical images and the therapeutic areas in which they were used. We included most relevant papers about the most used medical investigations, respectively medical images.…”

Section: Applications In Medicine and The Performance Of DL Models Depending On The Therapeutic Areas In Which They Were Usedmentioning

confidence: 99%

“…This paper presents a methodical review of the literature [1] with the objective of carrying out an analysis of the importance of the relationship between the types and characteristics of scientific data and their use of deep learning models in the interpretation of medical images. We have defined a methodology for semiautomating the production of relevant articles and eliminating those with low impact in the scientific community, by applying inclusive and exclusive quality criteria in the fields of medicine and information technology [2]. The major contribution of this work lies primarily in the updated characterization of the characteristics of the constituent elements of the process of deep learning from data to applications in medicine.…”

Section: Introductionmentioning

confidence: 99%

“…The large volume of training data and properly labeled determines the performance of the deep learning modeling in the interpretation of medical images [3]. Because manual image labelling requires time and specialized training, standardized, organized labelling has been used which has the risk of over-labeling with unnecessary information [2].…”

Section: Introductionmentioning

confidence: 99%

“…Applications and the performances of the DL models depending on the types of medical images and the therapeutic area[2]. Acronyms: AMD, age-related Macular Degeneration, CAD, Computer Aided Diagnosis, CNN, Con-volutional Neural Network, MRI, Magnetic Resonance Images, PET, Photon Emission Tomogra-phy, CT, Computed Tomography, OCT, Optical Coherence Tomography, D, dimensions, AUC, Area Under the Curve, MSE, Mean Squared Error, RMSE, Root Mean Square Error, DSC, Dice Similarity Coefficient[2].…”

mentioning

confidence: 99%

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Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Ursuleanu¹,

Luca²,

Gheorghe³

et al. 2021

ETSN

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Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

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Section: Applications In Medicine and The Performance Of DL Models Depending On The Therapeutic Areas In Which They Were Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Ursuleanu¹,

Luca²,

Gheorghe³

et al. 2021

ETSN

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“…Deep auto-encoders (AUD) are included in the type of unsupervised learning that uses unlabeled input data, there is no a priori knowledge, and the results to be obtained from the processing of input data are unknown, and can learn to organize information without providing an error calculation to evaluate the possible solution [36,37]. The main feature of the autoencoder is represented by the input and output layers have the same size, and the output must reproduce the input, while the hidden layers are smaller in size because the input patterns are progressively encoded and decoded throughout the process, and has the ability to extract the fundamental characteristics of the input, being used to reduce the size of the data, but also to reduce noise in input data (such as images).…”

Section: Architectures Designed For Diagnosis Classification Segmentation Detection and Reconstruction Of Medical Imagesmentioning

confidence: 99%

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

et al. 2021

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The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their “key” features, for completion of tasks in current applications in the interpretation of medical images. The use of “key” characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.

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Ensembled Deep Learning for the Classification of Human Sperm Head Morphology

Lindsay

Fernando

Akbaridoust

et al. 2022

Advanced Intelligent Systems

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Infertility is a growing global health concern, with male factor infertility contributing to half of all cases. Semen analysis is crucial to infertility diagnostics. However, sperm morphology assessment, as a routine part of analysis, is still performed manually and is thus highly subjective. Here, a stacked ensemble of convolutional neural networks (CNNs) is presented for automated classification of human sperm head morphology. By combining traditional CNN models with modern residual and densely connected architectures using a multi‐class meta‐classifier, classification rate improvements of 2.7% (to 98.2%) and 2.3% (to 63.3%) on the HuSHeM and SCIAN‐MorphoSpermGS (SCIAN) datasets, respectively, are achieved. This considerable improvement in prediction performance is achieved as the meta‐classifier improves upon the individual classification rates of the base models by ≈8.5%. The ensembled deep learning model is a powerful step toward an automated sperm morphology analysis, providing new opportunities to standardize clinical practice and reduce treatment costs to improve patient treatment.

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A survey of deep learning models in medical therapeutic areas

Cited by 36 publications

References 80 publications

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

Ensembled Deep Learning for the Classification of Human Sperm Head Morphology

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