patch-test
发表于 2025-3-21 19:09:00
书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0388172<br><br> <br><br>书目名称Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0388172<br><br> <br><br>
Adjourn
发表于 2025-3-21 20:30:41
Extended Graph Assessment Metrics for Regression and Weighted Graphssion tasks, as well as continuous adjacency matrices, and propose a lightweight CCNS distance for discrete and continuous adjacency matrices. We show the correlation of these metrics with model performance on different medical population graphs and under different learning settings, using the TADPOL
Kaleidoscope
发表于 2025-3-22 03:18:57
Multi-head Graph Convolutional Network for Structural Connectome Classification7 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.
墙壁
发表于 2025-3-22 05:24:34
Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion in oncology research. Additionally, we further illustrate the utility of the learned generative models for data augmentation in a TLS classification task. To the best of our knowledge, this is the first work that leverages the power of graph diffusion models in generating meaningful biological cell
Root494
发表于 2025-3-22 10:49:51
Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-structured reports generation and multi-label classification of pathologies. Our approach represents a promising method for generating radiology graphs directly from CXR images, and has significant potential for improving medical image analysis and clinical decision-making. Our code is open sourced
Psychogenic
发表于 2025-3-22 16:25:36
A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Agelation-graph construction methods and their effect on GNN performance on brain age estimation. We use the homophily metric and graph visualizations to gain valuable quantitative and qualitative insights on the extracted graph structures. For the experimental evaluation, we leverage the UK Biobank da
Psychogenic
发表于 2025-3-22 19:40:39
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Melodrama
发表于 2025-3-23 01:14:05
Multi-level Graph Representations of Melanoma Whole Slide Images for Identifying Immune SubgroupsMIL methods. Our experimental results comprehensively show how our whole slide image graph representation is a valuable improvement on the MIL paradigm and could help to determine early-stage prognostic markers and stratify melanoma patients for effective treatments. Code is available at ..
incite
发表于 2025-3-23 05:00:24
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上腭
发表于 2025-3-23 06:15:50
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