Titlebook: Medical Image Understanding and Analysis; 26th Annual Conferen Guang Yang,Angelica Aviles-Rivero,Carola-Bibiane S Conference proceedings 20

他剪短

書目名稱Medical Image Understanding and Analysis影響因子(影響力)




書目名稱Medical Image Understanding and Analysis影響因子(影響力)學科排名




書目名稱Medical Image Understanding and Analysis網(wǎng)絡公開度




書目名稱Medical Image Understanding and Analysis網(wǎng)絡公開度學科排名




書目名稱Medical Image Understanding and Analysis被引頻次




書目名稱Medical Image Understanding and Analysis被引頻次學科排名




書目名稱Medical Image Understanding and Analysis年度引用




書目名稱Medical Image Understanding and Analysis年度引用學科排名




書目名稱Medical Image Understanding and Analysis讀者反饋




書目名稱Medical Image Understanding and Analysis讀者反饋學科排名

潔凈

Multimodal Cardiomegaly Classification with?Image-Derived Digital Biomarkers-derived digital biomarkers, the cardiothoracic ratio (CTR) and the cardiopulmonary area ratio (CPAR). The CTR and CPAR values are estimated using segmentation and detection models. These are then integrated into a multimodal network trained simultaneously on chest radiographs and ICU data (vital si

BRUNT

Proton Density Fat Fraction of?Breast Adipose Tissue: Comparison of?the?Effect of?Fat Spectra and?Inmarker which has not yet been thoroughly examined in the characterisation of breast fat; this work therefore explores the estimation of breast-specific PDFF. An MR spectrum derived from healthy breast fat is shown to perform significantly better in PDFF calculation of breast adipose tissue amongst a

咽下

Revisiting the?Shape-Bias of?Deep Learning for?Dermoscopic Skin Lesion Classificationrowing body of work aiming to align deep models’ decision-making processes with the fundamental properties of human vision. The reliance on shape features is primarily expected to improve the robustness of these models under covariate shift. In this paper, we revisit the significance of . for the cl

Brocas-Area

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釘牢

CULP

Faster Diffusion Cardiac MRI with?Deep Learning-Based Breath Hold Reductionvo and non-invasively, which no other imaging modality allows. This innovative technology could revolutionise the ability to perform cardiac clinical diagnosis, risk stratification, prognosis and therapy follow-up. However, DT-CMR is currently inefficient with over six minutes needed to acquire a si

abreast

Condyle