Modifying u-net for small dataset: a simplified u-net version for liver parenchyma segmentation

Prasad, Pravda Jith Ray; Elle, Ole Jakob; Lindseth, Frank; Albregtsen, Fritz; Kumar, Rahul Prasanna

doi:10.1117/12.2582179

Cited by 7 publications

(6 citation statements)

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“…Furthermore, significant inequality always exists between the size of negative and positive samples, which may have a greater impact on the segmentation. However, U-Net could afford an approach achieving better performance in reducing overfitting [25].…”

Section: Existing Challengesmentioning

confidence: 99%

U-Net-Based Medical Image Segmentation

Yin

Sun

et al. 2022

Journal of Healthcare Engineering

195

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Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the performance of segmentation in medical imaging in recent years, U-Net has been cited academically more than 2500 times. Many scholars have been constantly developing the U-Net architecture. This paper summarizes the medical image segmentation technologies based on the U-Net structure variants concerning their structure, innovation, efficiency, etc.; reviews and categorizes the related methodology; and introduces the loss functions, evaluation parameters, and modules commonly applied to segmentation in medical imaging, which will provide a good reference for the future research.

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Section: Existing Challengesmentioning

confidence: 99%

U-Net-Based Medical Image Segmentation

Yin

Sun

et al. 2022

Journal of Healthcare Engineering

195

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“…It has been demonstrated in many studies, such as Prasad et al (2021), Conze et al (2021) and Liu et al (2022b), that most DL models cannot accurately segment liver tissues if (i) small training datasets are considered; and/or (ii) there is a discrepancy/data distribution inconsistency between training and testing datasets, where both datasets are not withdrawn from the same distribution (Zoetmulder et al, 2022). To that end, TL has recently received increasing attention due to its ability to (i) provide high-quality decision support; (ii) require less training data compared to conventional DL algorithms; and (iii) reduce the domain shift between source domain data and target domain data.…”

Section: Tl For Liver Delineationmentioning

confidence: 85%

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Al‐Kababji

Bensaali

Dakua

et al. 2023

Engineering Applications of Artificial Intelligence

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“…In 2021, Prasad et al 202 developed an automatic liver parenchyma segmentation network based on the U-Net architecture. The authors used a data set consisting of highly variable venous phase enhanced computed tomography (CT) volumes, with 10 males and 10 females as the source, 75% of whom had liver tumors.…”

Section: U-netmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

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Modifying u-net for small dataset: a simplified u-net version for liver parenchyma segmentation

Cited by 7 publications

References 0 publications

U-Net-Based Medical Image Segmentation

U-Net-Based Medical Image Segmentation

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Machine Learning Methods for Small Data Challenges in Molecular Science

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