Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

Yamada, Akira; Oyama, Kazuki; Fujita, Sachie; Yoshizawa, Eriko; Ichinohe, Fumihito; Komatsu, Daisuke; Fujinaga, Yasunari

doi:10.1007/s11548-019-01987-1

Cited by 20 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most researchers and clinicians require a process of trial and error to find out promising AI techniques fitting their target images 24 . The three models assessed in this study, AlexNet, VGG16, and InceptionV3, have been actively applied to the task of medical image classification 8,25,26 . More advanced models may be applicable, but such models generally require greater computational power.…”

Section: Discussionmentioning

confidence: 99%

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study

Mine

Iwamoto

Okazaki

et al. 2022

Int J Paed Dentistry

View full text Add to dashboard Cite

Background: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth. Aim: This study aimed to apply convolutional neural network (CNN)-based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage. Design: Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6 years 0 months to 9 years 6 months) including supernumerary teeth (cases, n = 120) or no anomalies (controls, n = 100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross-validation test dataset. Results:The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance. Conclusion:CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.

show abstract

Section: Discussionmentioning

confidence: 99%

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study

Mine

Iwamoto

Okazaki

et al. 2022

Int J Paed Dentistry

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Moreover, this work also shows that CT CNN models can improve accuracy, sensitivity and specificity compared to using CT alone (0.811–0.833, 0.744–0.923, and 0.725–0.941 vs. 0.543–0.676, 0.316–0.541 and 0.914, respectively). On a similar note, Yamada et al [ 42 ] proposed transfer learning of pre-trained CNNs (GoogLeNet and Inception-v3) on a dataset of 215 patients with histologically proven primary liver cancers. Their approach consisted in splitting in a RGB system the results of different CT phases, obtaining in each similar accuracy to experienced abdominal radiologists and higher than general radiologists.…”

Section: Diagnosismentioning

confidence: 99%

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

et al. 2021

View full text Add to dashboard Cite

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

show abstract

Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

Cited by 20 publications

References 8 publications

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study

A review of transfer learning for medical image classification

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Contact Info

Product

Resources

About