RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning

Zhou, Zhanping; Zhao, Chenyang; Qiao, Hui; Wang, Ming; Guo, Yuchen; Wang, Qian; Zhang, Rui; Wu, Huaiyu; Dong, Fajin; Zhang, Qi; Li, Jianchu; Tian, Xin; Zeng, Xiaofeng; Jiang, Yuxin; Xu, Feng; Dai, Qionghai; Yang, Meng

doi:10.1016/j.patter.2022.100592

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Meanwhile, extracting and fusing semantic features of solid tissues and calcification for better recognizing the segmented nodules. Zhou et al ( 2022 ) proposed a rheumatoid arthritis knowledge-guided (RATING) system for scoring rheumatoid arthritis activity from multimodal ultrasound images, leveraging diagnostic paradigm and experience to enhance the robustness. Lu et al ( 2023 ) proposed a Prior Knowledge-based Relation Transformer Network (PKRT-Net), which employed the clinical prior knowledge to assist OC segmentation.…”

Section: Related Workmentioning

confidence: 99%

STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition

Wang

Han

et al. 2023

Front. Neurosci.

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IntroductionThe human brain processes shape and texture information separately through different neurons in the visual system. In intelligent computer-aided imaging diagnosis, pre-trained feature extractors are commonly used in various medical image recognition methods, common pre-training datasets such as ImageNet tend to improve the texture representation of the model but make it ignore many shape features. Weak shape feature representation is disadvantageous for some tasks that focus on shape features in medical image analysis.MethodsInspired by the function of neurons in the human brain, in this paper, we proposed a shape-and-texture-biased two-stream network to enhance the shape feature representation in knowledge-guided medical image analysis. First, the two-stream network shape-biased stream and a texture-biased stream are constructed through classification and segmentation multi-task joint learning. Second, we propose pyramid-grouped convolution to enhance the texture feature representation and introduce deformable convolution to enhance the shape feature extraction. Third, we used a channel-attention-based feature selection module in shape and texture feature fusion to focus on the key features and eliminate information redundancy caused by feature fusion. Finally, aiming at the problem of model optimization difficulty caused by the imbalance in the number of benign and malignant samples in medical images, an asymmetric loss function was introduced to improve the robustness of the model.Results and conclusionWe applied our method to the melanoma recognition task on ISIC-2019 and XJTU-MM datasets, which focus on both the texture and shape of the lesions. The experimental results on dermoscopic image recognition and pathological image recognition datasets show the proposed method outperforms the compared algorithms and prove the effectiveness of our method.

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Section: Related Workmentioning

confidence: 99%

STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition

Wang

Han

et al. 2023

Front. Neurosci.

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A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

VanBerlo,

Hoey,

Wong

2024

BMC Med Imaging

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Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.

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RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning

Cited by 2 publications

References 33 publications

STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition

STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

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