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Wei Y, Deng Y, Sun C, Lin M, Jiang H, Peng Y. Deep learning with noisy labels in medical prediction problems: a scoping review. J Am Med Inform Assoc 2024; 31:1596-1607. [PMID: 38814164 PMCID: PMC11187424 DOI: 10.1093/jamia/ocae108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/27/2024] [Accepted: 05/03/2024] [Indexed: 05/31/2024] Open
Abstract
OBJECTIVES Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. METHODS Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical/healthcare/clinical," "uncertainty AND medical/healthcare/clinical," and "noise AND medical/healthcare/clinical." RESULTS A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. DISCUSSION From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.
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Affiliation(s)
- Yishu Wei
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
- Reddit Inc., San Francisco, CA 16093, United States
| | - Yu Deng
- Center for Health Information Partnerships, Northwestern University, Chicago, IL 10611, United States
| | - Cong Sun
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
| | - Mingquan Lin
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Hongmei Jiang
- Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
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Ji W, Yang F. Affine medical image registration with fusion feature mapping in local and global. Phys Med Biol 2024; 69:055029. [PMID: 38324893 DOI: 10.1088/1361-6560/ad2717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/07/2024] [Indexed: 02/09/2024]
Abstract
Objective. Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible with most real-time medical applications. On the other hand, convolutional neural networks are limited in modeling long-range spatial relationships of the features due to inductive biases, such as weight sharing and locality. This is not conducive to affine registration tasks. Therefore, the evolution of real-time and high-accuracy affine medical image registration algorithms is necessary for registration applications.Approach. In this paper, we propose a deep learning-based coarse-to-fine global and local feature fusion architecture for fast affine registration, and we use an unsupervised approach for end-to-end training. We use multiscale convolutional kernels as our elemental convolutional blocks to enhance feature extraction. Then, to learn the long-range spatial relationships of the features, we propose a new affine registration framework with weighted global positional attention that fuses global feature mapping and local feature mapping. Moreover, the fusion regressor is designed to generate the affine parameters.Main results. The additive fusion method can be adaptive to global mapping and local mapping, which improves affine registration accuracy without the center of mass initialization. In addition, the max pooling layer and the multiscale convolutional kernel coding module increase the ability of the model in affine registration.Significance. We validate the effectiveness of our method on the OASIS dataset with 414 3D MRI brain maps. Comprehensive results demonstrate that our method achieves state-of-the-art affine registration accuracy and very efficient runtimes.
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Affiliation(s)
- Wei Ji
- School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi, 530004, People's Republic of China
| | - Feng Yang
- School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi, 530004, People's Republic of China
- Guangxi Key Laboratory of Multimedia Communications Network Technology, Guangxi University, Nanning, Guangxi, 530004, People's Republic of China
- Key Laboratory of Parallel, Distributed and Intelligent Computing of Guangxi Universities and Colleges, Nanning, Guangxi, 530004, People's Republic of China
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Xing G, Wang S, Gao J, Li X. Real-time reliable semantic segmentation of thyroid nodules in ultrasound images. Phys Med Biol 2024; 69:025016. [PMID: 38048630 DOI: 10.1088/1361-6560/ad1210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 12/04/2023] [Indexed: 12/06/2023]
Abstract
Objective.Low efficiency in medical image segmentation is a common issue that limits computer-aided diagnosis development. Due to the varying positions and sizes of nodules, it is not easy to accurately segment ultrasound images. This study aims to propose a segmentation model that maintains high efficiency while improving accuracy.Approach. We propose a novel layer that integrates the advantages of dense connectivity, dilated convolution, and factorized filters to maintain excellent efficiency while improving accuracy. Dense connectivity optimizes feature reuse, dilated convolution redesigns layers, and factorized convolution improves efficiency. Moreover, we propose a loss function optimization method from a pixel perspective to increase the network's accuracy further.Main results.Experiments on the Thyroid dataset show that our method achieves 81.70% intersection-over-union (IoU), 90.50% true positive rate (TPR), and 0.25% false positive rate (FPR). In terms of accuracy, our method outperforms the state-of-the-art methods, with twice faster inference and nearly 400 times fewer parameters. Meanwhile, in a test on an External Thyroid dataset, our method achieves 77.03% IoU, 82.10% TPR, and 0.16% FPR, demonstrating our proposed model's robustness.Significance.We propose a real-time semantic segmentation architecture for thyroid nodule segmentation in ultrasound images called fully convolution dense dilated network (FCDDN). Our method runs fast with a few parameters and is suitable for medical devices requiring real-time segmentation.
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Affiliation(s)
- Guangxin Xing
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, People's Republic of China
| | - Shuaijie Wang
- College of Intelligence and Computing, Tianjin University, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Advanced Networking, Tianjin, People's Republic of China
| | - Jie Gao
- College of Intelligence and Computing, Tianjin University, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Advanced Networking, Tianjin, People's Republic of China
| | - Xuewei Li
- College of Intelligence and Computing, Tianjin University, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Advanced Networking, Tianjin, People's Republic of China
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Gökmen Inan N, Kocadağlı O, Yıldırım D, Meşe İ, Kovan Ö. Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107921. [PMID: 37950926 DOI: 10.1016/j.cmpb.2023.107921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/20/2023] [Accepted: 11/06/2023] [Indexed: 11/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Early detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15 %, while some studies indicate a risk as high as 25 %. Without complete histology, it is difficult to classify nodules and these diagnostic operations are pricey and risky. To minimize laborious workload and misdiagnosis, recently various AI-based decision support systems have been developed. METHODS In this study, a novel AI-based decision support system has been developed for the automated segmentation and classification of the types of thyroid nodules. This system is based on a hybrid deep-learning procedure that makes both an automatic thyroid nodule segmentation and classification tasks, respectively. In this framework, the segmentation is executed with some U-Net architectures such as ResUNet and ResUNet++ integrating with the feature extraction and upsampling with dropout operations to prevent overfitting. The nodule classification task is achieved by various deep nets architecture such as VGG-16, DenseNet121, ResNet-50, and Inception ResNet-v2 considering some accurate classification criteria such as Intersection over Union (IOU), Dice coefficient, accuracy, precision, and recall. RESULTS In analysis, a total of 880 patients with ages ranging from 10 to 90 years were included by taking the ultrasound images and demographics. The experimental evaluations showed that ResUNet++ demonstrated excellent segmentation outcomes, attaining remarkable evaluation scores including a dice coefficient of 92.4 % and a mean IOU of 89.7 %. ResNet-50 and Inception ResNet-v2 trained over the images segmented with UNets have shown better performance in terms of achieving high evaluation scores for the classification accuracy such as 96.6 % and 95.0 %, respectively. In addition, ResNet-50 and Inception ResNet-v2 classified AUS/FLUS from the images segmented with UNets with AUC=97.0 % and 96.0 %, respectively. CONCLUSIONS The proposed AI-based decision support system improves the automatic segmentation performance of AUS/FLUS and it has shown better performance than available approaches in the literature with respect to ACC, Jaccard and DICE losses. This system has great potential for clinical use by both radiologists and surgeons as well.
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Affiliation(s)
- Neslihan Gökmen Inan
- College of Engineering, Computer Engineering Department, Koç University, Türkiye
| | - Ozan Kocadağlı
- Department of Statistics, Faculty of Science and Letters, Mimar Sinan Fine Arts University, Silahsör Cad. No. 81, 34380 Bomonti/Sisli, Istanbul, Türkiye.
| | | | - İsmail Meşe
- Department of Radiology, Erenkoy Mental Health and Neurology Training and Research Hospital, Health Sciences University, Türkiye
| | - Özge Kovan
- Vocational School of Health Services, Medical Imaging Techniques, Acıbadem University, Türkiye
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Shankarlal B, Dhivya S, Rajesh K, Ashok S. A hybrid thyroid tumor type classification system using feature fusion, multilayer perceptron and bonobo optimization. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:651-675. [PMID: 38393884 DOI: 10.3233/xst-230430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
BACKGROUND Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors. OBJECTIVES This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques. The input data set is obtained from Digital Database of Thyroid Ultrasound Images through Kaggle repository and augmented for achieving a better classification performance using data warping mechanisms like flipping, rotation, cropping, scaling, and shifting. METHODS The input data after augmentation goes through preprocessing with the help of bilateral filter and is contrast enhanced using dynamic histogram equalization. The ultrasound images are then segmented using SegNet algorithm of convolutional neural network. The features needed for thyroid tumor classification are obtained from two different algorithms called CapsuleNet and EfficientNetB2 and both the features are fused together. This process of feature fusion is carried out to heighten the accuracy of classification. RESULTS A Multilayer Perceptron Classifier is used for classification and Bonobo optimizer is employed for optimizing the results produced. The classification performance of the proposed model is weighted using metrics like accuracy, sensitivity, specificity, F1-score, and Matthew's correlation coefficient. CONCLUSION It can be observed from the results that the proposed multilayer perceptron based thyroid tumor type classification system works in an efficient manner than the existing classifiers like CANFES, Spatial Fuzzy C means, Deep Belief Networks, Thynet and Generative adversarial network and Long Short-Term memory.
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Affiliation(s)
- B Shankarlal
- Department of Electrical and Computer Engineering, Perunthalaivar Kamarajar Institute of Engineering and Technology, Karaikal, India
| | - S Dhivya
- Department of Electrical and Computer Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India
| | - K Rajesh
- Department of Electrical and Computer Engineering, SSM Institute of Engineering and Technology, Kuttathupatti, Dindigul, India
| | - S Ashok
- Department of Electrical and Computer Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai, India
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