Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT

Sasaki, Yuki; Kondo, Yohan; Aoki, Tadashi; Koizumi, Noriko; Ozaki, Toshirou; Seki, Hiroshi

doi:10.1007/s11548-022-02694-0

Cited by 5 publications

(6 citation statements)

References 34 publications

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“…(i) Source domain and target domain: the related works [9][10][11][12] formulated the transfer learning problem using a similar source domain and target domain whereas other works [13][14][15][16] considered the distant source and target domains. Our work considered 10 benchmark datasets to evaluate the MTL using similar and distant sources and target domains.…”

Section: Performance Comparison With Related Workmentioning

confidence: 99%

“…It is important to report both the sensitivity and specifcity to ensure that biased classifcation is not observed. Te works [13,16] reported the sensitivity of the LCD model when transfer learning is applied. Te work [12] revealed the improvement of sensitivity by 2.22% using the transfer learning model.…”

Section: Performance Comparison With Related Workmentioning

confidence: 99%

“…(viii) Accuracy: all related works and our work report the accuracy. Related works [13][14][15][16] only reported the results after applying the transfer learning model. Te percentage improvement of the accuracy is 7.80% [9], 3.77% [10], 1.34% [11], 5.88% [12], and 6.85-9.92% (our work).…”

Section: Performance Comparison With Related Workmentioning

confidence: 99%

“…Related Works. Although existing works [9][10][11][12][13][14][15][16] formulated the transfer learning problems with a single source domain and single target domain, the discussion has merit as these works fell into the same research area, i.e., transfer learning for LCD. In the following, two common types of formulations will be discussed: (i) transfer learning between the similar source domain and target domain [9][10][11][12] and (ii) transfer learning between the distant source domain and target domain [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Te model was with 98.8% and 83.4% of training accuracy and testing accuracy, respectively. ImageNet was served as the source domain for the knowledge transfer of a VGG19 pretrained model to the target domain of 150 patients with CT scans [16]. Te model achieved sensitivity, specifcity, and accuracy of 75%, 87%, and 82%, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection

Chui¹,

Gupta

Jhaveri

et al. 2023

International Journal of Intelligent Systems

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Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an accurate LCD model are the small-scale datasets, the poor generalizability to detect unseen data, and the selection of useful source domains and prioritization of multiple source domains for transfer learning. In this paper, a multiround transfer learning and modified generative adversarial network (MTL-MGAN) algorithm is proposed for LCD. The MTL transfers the knowledge between the prioritized source domains and target domain to get rid of exhaust search of datasets prioritization among multiple datasets, maximizing the transferability with a multiround transfer learning process, and avoiding negative transfer via customization of loss functions in the aspects of domain, instance, and feature. In regard to the MGAN, it not only generates additional training data but also creates intermediate domains to bridge the gap between the source domains and target domains. 10 benchmark datasets are chosen for the performance evaluation and analysis of the MTL-MGAN. The proposed algorithm has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the MTL-MGAN, ablation studies are conducted to confirm the effectiveness of the prioritization algorithm, the MTL, the negative transfer avoidance via loss functions, and the MGAN. The research implications are to confirm the feasibility of multiround transfer learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the source domain and target domain using MGAN.

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