Time Trends of Cardiovascular Disease in the General Population and Inflammatory Arthritis

Södergren, Anna; Rantapää-Dahlqvist, Solbritt; Ljung, Lotta

doi:10.1016/j.rdc.2022.07.003

Cited by 3 publications

(2 citation statements)

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“…Similar to deep supervision, this approach has one primary loss, along with two auxiliary losses for network supervision. Equation (1) shows that the loss functions are all typical crossentropy losses.…”

Section: Loss Functionmentioning

confidence: 99%

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Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Kumar

Song

et al. 2023

Life

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Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness.

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Section: Loss Functionmentioning

confidence: 99%

“…Cardiovascular disease, whose global mortality has been rising, is considered a severe threat to the health of humankind [1]. Accordingly, growing attention has been paid to research and technology pertaining to the improvement of cardiac detection and reduction in the mortality rate associated with these diseases.…”

Section: Introductionmentioning

confidence: 99%