Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks

Saito, Namiko; Ogata, Tetsuya; Mori, Hiroki; Murata, Satoshi; Sugano, Shigeki

doi:10.3389/frobt.2021.748716

Cited by 4 publications

(3 citation statements)

References 41 publications

(52 reference statements)

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“…This improves network reliability and accurately represents the input image, addressing the problem of the "black box" principle. [28] In this study, we developed an efficient deep learning method that eliminates the need for manual feature recognition, reducing the workload of diagnosing pneumoconiosis (the outline of the study design is given in Fig. 1).…”

Section: This Study Was Supported By the High-level Talent Scientific...mentioning

confidence: 99%

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Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study

Zhang,

Zheng,

Zeng

et al. 2024

Medicine

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The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoconiosis diagnoses. Three models (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1250 chest X-ray images. Three experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III in a double-blinded manner. The results of the 3 physicians in agreement were considered the relative gold standards. Subsequently, 3 models were used to train and test these images and their performance was evaluated using multi-class classification metrics. We used kappa values and accuracy to evaluate the consistency and reliability of the optimal model with clinical typing. The results showed that ResNet101 was the optimal model among the 3 convolutional neural networks. The AUC of ResNet101 was 1.0, 0.9, 0.89, and 0.94 for detecting pneumoconiosis categories 0, I, II, and III, respectively. The micro-average and macro-average mean AUC values were 0.93 and 0.94, respectively. The accuracy and Kappa values of ResNet101 were 0.72 and 0.7111 for quadruple classification and 0.98 and 0.955 for dichotomous classification, respectively, compared with the relative standard classification of the clinic. This study develops a deep learning based model for screening and staging of pneumoconiosis is using chest radiographs. The ResNet101 model performed relatively better in classifying pneumoconiosis than radiologists. The dichotomous classification displayed outstanding performance, thereby indicating the feasibility of deep learning techniques in pneumoconiosis screening.

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Section: This Study Was Supported By the High-level Talent Scientific...mentioning

confidence: 99%

“…This improves network reliability and accurately represents the input image, addressing the problem of the “black box” principle. [ 28 ]…”

Section: Introductionmentioning

confidence: 99%

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study

Zhang,

Zheng,

Zeng

et al. 2024

Medicine

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“…The deep learning network is an advanced technique for studying the aforementioned features and can offer heat maps that visualize the interpretation of model decisions. Thus, an improvement of the network reliability and an accurate representation of the content of the input image data solves the problem of the "black box" principle [20]. By comparing the performance of three convolutional neural networks (CNNs), we aimed to establish an end-to-end automatic pneumoconiosis medical image classi cation model based on deep learning and evaluate the feasibility of the model for pneumoconiosis classi cation.…”

Section: Introductionmentioning

confidence: 99%

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis

Zhang

Zheng

et al. 2023

Preprint

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Background To improve the accuracy of pneumoconiosis diagnosis, a computer-assisted method was developed. Methods Three CNNs (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1,250 chest X-ray images. Three double-blinded experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III. The results of the three physicians in agreement were considered the relative gold standards. Subsequently, three CNNs were used to train and test these images and their performance was evaluated using multi-class classification metrics. We used kappa values and accuracy to evaluate the consistency and reliability of the optimal model with clinical typing. Results ResNet101 was the optimal model among the three CNNs. The AUC of ResNet101 was 1.0, 0.9, 0.89, and 0.94 for detecting pneumoconiosis categories 0, I, II, and III, respectively. The micro-average and macro-average mean AUC values were 0.93 and 0.94, respectively. The accuracy and Kappa values of ResNet101 were 0.72 and 0.7111 for quadruple classification and 0.98 and 0.955 for dichotomous classification, respectively, compared with the relative standard classification of the clinic. Conclusion The ResNet101 model performed relatively better in classifying pneumoconiosis than radiologists. The dichotomous classification displayed outstanding performance, thereby indicating the feasibility of deep learning techniques in pneumoconiosis screening.

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Multi-step planning with learned effects of partial action executions

Aktas,

Bozdogan,

Ugur

2024

Advanced Robotics

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Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks

Cited by 4 publications

References 41 publications

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis

Multi-step planning with learned effects of partial action executions

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