Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

Jefferson

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021影響因子(影響力)




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021影響因子(影響力)學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021網(wǎng)絡(luò)公開度




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021網(wǎng)絡(luò)公開度學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021被引頻次




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021被引頻次學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021年度引用




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021年度引用學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021讀者反饋




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021讀者反饋學科排名

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心痛

Colorectal Polyp Classification from White-Light Colonoscopy Images via?Domain Alignmentfrom colonoscopy images. Most previous studies attempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. However, the wide range of these models’ applications for clinical work has been limited by the lagging of imaging techniques. Thus, we propose

Myocyte

Mindfulness

Deep-Cleansing: Deep-Learning Based Electronic Cleansing in Dual-Energy CT Colonographyc surface in CT colonography (CTC). This paper introduced a deep-learning based EC method in dual-energy CTC (DE-CTC), named “Deep-Cleansing”. First, we calculated the “effective” atomic number (EAN) by fractions of atomic mass number using the low- and high-energy images in DE-CT. Second, multiple

BET

dragon

Synthesis of Contrast-Enhanced Spectral Mammograms from Low-Energy Mammograms Using cGAN-Based Synthhigh predictive value. However, the iodinated contrast media injected during CESM examination can cause adverse reactions, such as allergic reactions, and even cause contra-induced nephropathy. Therefore, iodinated contrast media cannot be used for some patients. To address this problem, we develope

廣大

Self-adversarial Learning for Detection of?Clustered Microcalcifications in?Mammogramso-step paradigm: segmenting each MC and analyzing their spatial distributions to form MC clusters. However, segmentation of MCs cannot avoid low sensitivity or high false positive rate due to their variability in size (sometimes <0.1?mm), brightness, and shape (with diverse surroundings). In this pa

lethal

Graph Transformers for Characterization and Interpretation of Surgical Marginse. Considering the interpretability of Transformer models, and the power of graph networks in analyzing the inherent hierarchy of biological signals, a combined approach would be the next generation solution in computer aided interventions. In this study, we propose a framework for classification an

DRILL