Transfer learning for informative-frame selection in laryngoscopic videos through learned features

Patrini, Ilaria; Ruperti, Michela; Moccia, Sara; Mattos, Leonardo S.; Frontoni, Emanuele; Momi, Elena De

doi:10.1007/s11517-020-02127-7

Cited by 34 publications

(29 citation statements)

References 43 publications

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“…The use of artificial intelligence in musculoskeletal US may further increase its reproducibility and may save sonographers time as shown in cardiological setting (45). The correct acquisition of an US image is the essential step to ensure an accurate and reliable assessment of the image itself (31,39,46). Thus, we believe that the availability of an algorithm facilitating the identification of the region of interest during the acquisition process of US images represents a further step toward the standardization of US examination.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study

et al. 2021

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Objectives: This study aims to develop an automatic deep-learning algorithm, which is based on Convolutional Neural Networks (CNNs), for ultrasound informative-image selection of hyaline cartilage at metacarpal head level. The algorithm performance and that of three beginner sonographers were compared with an expert assessment, which was considered the gold standard.Methods: The study was divided into two steps. In the first one, an automatic deep-learning algorithm for image selection was developed using 1,600 ultrasound (US) images of the metacarpal head cartilage (MHC) acquired in 40 healthy subjects using a very high-frequency probe (up to 22 MHz). The algorithm task was to identify US images defined informative as they show enough information to fulfill the Outcome Measure in Rheumatology US definition of healthy hyaline cartilage. The algorithm relied on VGG16 CNN, which was fine-tuned to classify US images in informative and non-informative ones. A repeated leave-four-subject out cross-validation was performed using the expert sonographer assessment as gold-standard. In the second step, the expert assessed the algorithm and the beginner sonographers' ability to obtain US informative images of the MHC.Results: The VGG16 CNN showed excellent performance in the first step, with a mean area (AUC) under the receiver operating characteristic curve, computed among the 10 models obtained from cross-validation, of 0.99 ± 0.01. The model that reached the best AUC on the testing set, which we named “MHC identifier 1,” was then evaluated by the expert sonographer. The agreement between the algorithm, and the expert sonographer was almost perfect [Cohen's kappa: 0.84 (95% confidence interval: 0.71–0.98)], whereas the agreement between the expert and the beginner sonographers using conventional assessment was moderate [Cohen's kappa: 0.63 (95% confidence interval: 0.49–0.76)]. The conventional obtainment of US images by beginner sonographers required 6.0 ± 1.0 min, whereas US videoclip acquisition by a beginner sonographer lasted only 2.0 ± 0.8 min.Conclusion: This study paves the way for the automatic identification of informative US images for assessing MHC. This may redefine the US reliability in the evaluation of MHC integrity, especially in terms of intrareader reliability and may support beginner sonographers during US training.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study

et al. 2021

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“…This is a particularly good practice of science because solutions are shared online for the sake of reproducibility with the dataset. Popular datasets are listed in alphabetical order: BRATS is a dataset that provides The search string applied in Web of Science database was as follows: TS=("CNN" OR "convolutional") AND TS=("medical imag*" OR "clinical imag*" OR "biomedical imag*") AND TS=("transfer learning" OR "pre-trained" OR "pretrained") NOT TS=("novel" OR "propose") Alimentary system Feature extractor [34,35] Fine-tuning scratch [36,37] Bones Feature extractor [38] Genital systems Fine-tuning scratch [39] Nervous system Many [40] Respiratory system Feature extractor [41] Feature extractor hybrid [42] Fine-tuning scratch [43][44][45] Many [46,47] Sense organs Feature extractor [48] Thoracic cavity Feature extractor [49] Endoscopy Alimentary system Feature extractor [50,51] Fine-tuning scratch [52][53][54] Many [55] Mammographic Integumentary system Feature extractor [2]…”

Section: Discussionmentioning

confidence: 99%

A review of transfer learning for medical image classification

Kim

Cosa‐Linan

Maros

et al. 2021

Preprint

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This review paper provides an overview of the peer-reviewed articles using transfer learning for medical image analysis, while also providing guidelines for selecting a convolutional neural network model and its configurations for the image classification task. The data characteristics and the trend of models and transfer learning types in the medical domain are additionally analyzed. Publications were retrieved from the databases PubMed and Web of Science of peer-reviewed articles published in English until December 31, 2020. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. With respect to the model, the majority of studies (n = 57) empirically evaluated numerous models followed by deep (n = 33) and shallow (n = 24) models. With respect to the transfer learning approaches, the majority of studies (n = 46) empirically searched for the optimal transfer learning configuration followed by feature extractor (n = 38) and fine-tuning scratch (n = 27), feature extractor hybrid (n = 7) and fine-tuning (n = 3). The investigated studies showed that transfer learning demonstrates either a better or at least a similar performance compared to medical experts despite the limited data sets. We hence encourage data scientists and practitioners to use models such as ResNet or Inception with a feature extractor approach, which saves computational costs and time without degrading the predictive power.

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“…Deep neural networks have been used to develop classification models on a variety of modalities including MRI [88,89], DTI [90], CT [91], PET [60], radiographs [92] as well as on videos [93] as shown in Table 2. Complex networks are graphs described by pairs of nodes and links that represent the elements of the system to be modelled and the iterations between the same, respectively, and allow to measure particularly informative topological features.…”

Section: Used Software Toolsmentioning

confidence: 99%

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

Avanzo

Porzio²,

Lorenzon

et al. 2021

Physica Medica

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To perform a systematic review on the research on the application of artificial intelligence (AI) to imaging published in Italy and identify its fields of application, methods and results. Materials and Methods: A Pubmed search was conducted using terms Artificial Intelligence, Machine Learning, Deep learning, imaging, and Italy as affiliation, excluding reviews and papers outside time interval 2015-2020. In a second phase, participants of the working group AI4MP on Artificial Intelligence of the Italian Association of Physics in Medicine (AIFM) searched for papers on AI in imaging. Results: The Pubmed search produced 794 results. 168 studies were selected, of which 122 were from Pubmed search and 46 from the working group. The most used imaging modality was MRI (44%) followed by CT(12%) ad radiography/mammography (11%). The most common clinical indication were neurological diseases (29%) and diagnosis of cancer (25%). Classification was the most common task for AI (57%) followed by segmentation (16%). 65% of studies used machine learning and 35% used deep learning. We observed a rapid increase of research in Italy on artificial intelligence in the last 5 years, peaking at 155% from 2018 to 2019.

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Transfer learning for informative-frame selection in laryngoscopic videos through learned features

Cited by 34 publications

References 43 publications

Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study

Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study

A review of transfer learning for medical image classification

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

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