Self-supervised representation learning for detection of ACL tear injury in knee MR videos

Manna, Siladittya; Bhattacharya, Sumanta; Pal, Umapada

doi:10.1016/j.patrec.2022.01.008

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…After proofing the effectiveness of using the preprocessing in our work, we need to validate it more through the outcomes with some other modern approaches. In this study, the outcomes are compared with Convolutional Neural Network (CNN) 34 , Inception-v3 35 , Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA) 22 , integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO) 36 , Self-Supervised Representation Learning (SSRL) 37 .…”

Section: Resultsmentioning

confidence: 99%

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

Wang,

Yan

2024

Sci Rep

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In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions.

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Section: Resultsmentioning

confidence: 99%

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

Wang,

Yan

2024

Sci Rep

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“…Innate relationship was used in 15 out of 79 studies (Table 1 ). Nine of these studies designed their innate relationship pre-text task based on different image transformations, including rotation prediction 44 – 47 , horizontal flip prediction 48 , reordering shuffled slices 49 , and patch order prediction 46 , 50 – 52 . Notably, Jiao et al pre-trained their models simultaneously with two innate relationship pre-text tasks (slice order prediction and geometric transformation prediction), and showed that a weight-sharing Siamese network out-performs a single disentanged model for combining the two pre-training objectives 53 .…”

Section: Resultsmentioning

confidence: 99%

Self-supervised learning for medical image classification: a systematic review and implementation guidelines

et al. 2023

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Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data, which is both time-consuming and cost-prohibitive to curate for medical images. Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models through its ability to learn useful insights from copious medical datasets without labels. In this review, we provide consistent descriptions of different self-supervised learning strategies and compose a systematic review of papers published between 2012 and 2022 on PubMed, Scopus, and ArXiv that applied self-supervised learning to medical imaging classification. We screened a total of 412 relevant studies and included 79 papers for data extraction and analysis. With this comprehensive effort, we synthesize the collective knowledge of prior work and provide implementation guidelines for future researchers interested in applying self-supervised learning to their development of medical imaging classification models.

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“…Due to the big difference between the distribution of medical images and natural images, how to effectively apply the existing SSL frameworks to solve medical image analysis tasks has become a research hotspot. While some works have attempted to design domain-specific pretext tasks 42 – 46 , other works try to exploit improved version of the existing advanced contrastive learning frameworks to medical data 13 , 14 , 16 , 47 – 53 .…”

Section: Related Workmentioning

confidence: 99%

Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection

Gan,

Jin,

et al. 2023

Sci Rep

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Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representations among colonoscopic images, which is important for dense prediction tasks like polyp detection. Next, a self-supervised visual feature representation containing the general feature of colonoscopic images is learned by the siamese networks. Finally, the feature representation will be transferred to the downstream colorectal polyp detection task. A total of 103 videos (861,400 frames), 100 videos (24,789 frames), and 60 videos (15,397 frames) in the LDPolypVideo dataset are used to pre-train, train, and test the performance of the proposed FPSiam and its counterparts, respectively. The experimental results have illustrated that our FPSiam approach obtains the optimal capability, which is better than that of other state-of-the-art self-supervised learning methods and is also higher than the method based on transfer learning by 2.3 mAP and 3.6 mAP for two typical detectors. In conclusion, FPSiam provides a cost-efficient solution for developing colorectal polyp detection systems, especially in conditions where only a small fraction of the dataset is labeled while the majority remains unlabeled. Besides, it also brings fresh perspectives into other endoscopic image analysis tasks.

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Self-supervised representation learning for detection of ACL tear injury in knee MR videos

Cited by 10 publications

References 13 publications

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

Self-supervised learning for medical image classification: a systematic review and implementation guidelines

Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection

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