Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification

Lee, Juhun; Nishikawa, Robert M.

doi:10.1109/access.2020.3038909

Cited by 13 publications

(7 citation statements)

References 40 publications

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“…Various studies have implemented 2D–3D hybrid neural networks for organ segmentation [ 60 – 62 ], combining the semantic information of single slices extracted by 2D methods and the contextual semantic information extracted by 3D methods to achieve better segmentation results. Lee et al [ 63 ] incorporated migration learning into organ segmentation. All of these schemes offer potential research ideas for accurately segmenting head and neck OARs.…”

Section: Discussionmentioning

confidence: 99%

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Liu,

Sun,

Zhao

et al. 2023

BioMed Eng OnLine

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Purpose The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs. Methods This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type. Results 149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86/0.92, 0.92/0.90, 0.71/0.73, and 0.84/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88/0.87, 0.92/0.92, 0.75/0.71 and 0.87/0.85. Conclusions The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing "precision radiotherapy". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation.

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Section: Discussionmentioning

confidence: 99%

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Liu,

Sun,

Zhao

et al. 2023

BioMed Eng OnLine

View full text Add to dashboard Cite

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“…the Differential Evolution based Fine-Tuning (DEFT) techniques are proposed to tune the layers in the learning model in order to increase efficiency. Lee et al 2020 [17] proposed cross image modality-based TL for accurate classification of medical images. The different modality includes brain MRI and mammogram tumour images.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Transfer Learning Model Based Kidney Stone Classification

Mahalakshmi¹

2023

Computer Systems Science and Engineering

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The kidney is an important organ of humans to purify the blood. The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood. Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases. Early prediction of kidney stones is compulsory for timely treatment. Image processingbased diagnosis approaches provide a greater success rate than other detection approaches. In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL). The Deep Convolutional Neural Network (DCNN) models of DenseNet169, MobileNetv2 and GoogleNet applied for classification. The combined classification results are processed by ensemble learning to increase classification performance. The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer (GTO). The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.

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“…The knowledge gained from natural images (basic image features) is transferred to the bridge domain to learn the abstract features of the bridge domain. Finally, based on the learned features from two domains (source domain and bridge domain), the model is fine-tuned with the images in the target domain [43]. This particular model is applied for the different tasks in the target domain.…”

Section: Overall Framework Of the Multi-level Transfer Learning For T...mentioning

confidence: 99%

A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning

2023

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In the past few years, deep learning has gained increasingly widespread attention and has been applied to diagnosing benign and malignant thyroid nodules. It is difficult to acquire sufficient medical images, resulting in insufficient data, which hinders the development of an efficient deep-learning model. In this paper, we developed a deep-learning-based characterization framework to differentiate malignant and benign nodules from the thyroid ultrasound images. This approach improves the recognition accuracy of the inception network by combining squeeze and excitation networks with the inception modules. We have also integrated the concept of multi-level transfer learning using breast ultrasound images as a bridge dataset. This transfer learning approach addresses the issues regarding domain differences between natural images and ultrasound images during transfer learning. This paper aimed to investigate how the entire framework could help radiologists improve diagnostic performance and avoid unnecessary fine-needle aspiration. The proposed approach based on multi-level transfer learning and improved inception blocks achieved higher precision (0.9057 for the benign class and 0.9667 for the malignant class), recall (0.9796 for the benign class and 0.8529 for malignant), and F1-score (0.9412 for benign class and 0.9062 for malignant class). It also obtained an AUC value of 0.9537, which is higher than that of the single-level transfer learning method. The experimental results show that this model can achieve satisfactory classification accuracy comparable to experienced radiologists. Using this model, we can save time and effort as well as deliver potential clinical application value.

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Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification

Cited by 13 publications

References 40 publications

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

An Optimized Transfer Learning Model Based Kidney Stone Classification

A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning

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