Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network

Nishio, Motohiro; Fujimoto, Koji; Matsuo, Hiroo; Muramatsu, Chisako; Sakamoto, Ryo; Fujita, Hiroshi

doi:10.3389/frai.2021.694815

Cited by 27 publications

(13 citation statements)

References 34 publications

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“…Due to variation in size, appearance, and disturbances, unsupervised methods (Liu et al, 2014) are severely limited. Therefore, it is more common to use supervised methods (Mizuho et al, 2021). Yang (Yang et al, 2017) proposed an architecture which uses Fast R-CNN to realize the identification and classification of ships.…”

Section: Methodsmentioning

confidence: 99%

An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection

Zhou

Sun

et al. 2021

Front. Neurorobot.

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Water surface object detection is one of the most significant tasks in autonomous driving and water surface vision applications. To date, existing public large-scale datasets collected from websites do not focus on specific scenarios. As a characteristic of these datasets, the quantity of the images and instances is also still at a low level. To accelerate the development of water surface autonomous driving, this paper proposes a large-scale, high-quality annotated benchmark dataset, named Water Surface Object Detection Dataset (WSODD), to benchmark different water surface object detection algorithms. The proposed dataset consists of 7,467 water surface images in different water environments, climate conditions, and shooting times. In addition, the dataset comprises a total of 14 common object categories and 21,911 instances. Simultaneously, more specific scenarios are focused on in WSODD. In order to find a straightforward architecture to provide good performance on WSODD, a new object detector, named CRB-Net, is proposed to serve as a baseline. In experiments, CRB-Net was compared with 16 state-of-the-art object detection methods and outperformed all of them in terms of detection precision. In this paper, we further discuss the effect of the dataset diversity (e.g., instance size, lighting conditions), training set size, and dataset details (e.g., method of categorization). Cross-dataset validation shows that WSODD significantly outperforms other relevant datasets and that the adaptability of CRB-Net is excellent.

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Section: Methodsmentioning

confidence: 99%

An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection

Zhou

Sun

et al. 2021

Front. Neurorobot.

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“…Various methods (Isola et al, 2017 ; Han et al, 2018 ; Xue et al, 2018 ; Choi et al, 2019 ; Dong et al, 2019 ; Oh et al, 2020 ; Ding et al, 2021 ; He et al, 2021 ; Nishio et al, 2021 ; Wang T. et al, 2021 ; Zhan et al, 2021 ; Asis-Cruz et al, 2022 ) were proposed to explore the possibility of GAN in medical image segmentation. Xue et al ( 2018 ) used U-Net as the generator and proposed a multi-scale L 1 loss to minimize the distance of the feature maps of predictions and masks for the medical image segmentation of brain tumors.…”

Section: Related Workmentioning

confidence: 99%

A transformer-based generative adversarial network for brain tumor segmentation

et al. 2022

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Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which is trained in min–max game progress. The generator is based on a typical “U-shaped” encoder–decoder architecture, whose bottom layer is composed of transformer blocks with Resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale L1 loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted exclusive experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods. On additional datasets, including BRATS2018 and BRATS2020, experimental results prove that our technique is capable of generalizing successfully.

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“…MLbased tissue and tumor segmentation models have been widely proposed as auxiliary diagnostic tools. Two examples of the recent work on segmentation are Nazir et al [13] and Nishio et al [14] . In Nazir et al [13] approach , adaptive global threshold is applied for lung segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Adaptive Sparse representation is used for image fusion. Nishio et al [14] used transfer learning pretrained on an artificial dataset LUNA16. The artificial dataset was generated with the aid of Generative Adversial Network and 3D graph cut.…”

Section: Related Workmentioning

confidence: 99%

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

2022

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Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14.

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Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network

Cited by 27 publications

References 34 publications

An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection

An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection

A transformer-based generative adversarial network for brain tumor segmentation

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

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