Titlebook: Biomedical Engineering Systems and Technologies; 13th International J Xuesong Ye,Filipe Soares,Hugo Gamboa Conference proceedings 2021 Spri

amateur

書目名稱Biomedical Engineering Systems and Technologies影響因子(影響力)




書目名稱Biomedical Engineering Systems and Technologies影響因子(影響力)學(xué)科排名




書目名稱Biomedical Engineering Systems and Technologies網(wǎng)絡(luò)公開度




書目名稱Biomedical Engineering Systems and Technologies網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Biomedical Engineering Systems and Technologies被引頻次




書目名稱Biomedical Engineering Systems and Technologies被引頻次學(xué)科排名




書目名稱Biomedical Engineering Systems and Technologies年度引用




書目名稱Biomedical Engineering Systems and Technologies年度引用學(xué)科排名




書目名稱Biomedical Engineering Systems and Technologies讀者反饋




書目名稱Biomedical Engineering Systems and Technologies讀者反饋學(xué)科排名

vibrant

Pcos971

不朽中國

dragon

Non-invasive Optical Methods in Quantitative Minimal Erythema Dose Assessment in Vivo: Comparison ofty based on the calculation of minimal erythema dose (MED) is still performed visually, which is subjective, and associated with high variability of the results and frequent errors when it done be untrained personnel. The application of non-invasive quantitaitve methods such as laser fluorescence sp

periodontitis

Deep Learning for the Automated Feature Labelling of 3-Dimensional Imaged Placentalinked to chronic disease risk and quality of lifelong health. 2-D analysis can be challenging, and spatial interaction between structures can be easily missed, but obtaining 3-D structural images is extremely labour-intensive due to the high level of rigorous manual processing required. Deep neural

nepotism

Estimating the False Positive Prediction Rate in Automated Volumetric Measurements of Malignant Pleu, exhibiting an irregular shape with high surface-to-volume ratio. Reliable measurements are important to assessing treatment efficacy, however these tumour characteristics make manual measurements time consuming, and prone to intra- and inter-observer variation. Previously we described a fully auto

漂亮

Combining Registration Errors and Supervoxel Classification for Unsupervised Brain Anomaly Detection as the large variability in shape, size, and location among different anomalies. Even though discriminative models (supervised learning) are commonly used for this task, they require quite high-quality annotated training images, which are absent for most medical image analysis problems. Inspired by

使隔離

ADJ

Efficient Algorithms for Co-folding of Multiple RNAsent RNA sequences. For each permutation of the . strands the structure prediction problem is algorithmically very similar – but not identical – to folding of a single, contiguous RNA. The differences arise from two sources: First, “nicks”, i.e., the transitions from one to the next piece of RNA, nee