H&E stain
PulseAugur coverage of H&E stain — every cluster mentioning H&E stain across labs, papers, and developer communities, ranked by signal.
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深度学习从病理图像预测乳腺癌亚型
研究人员开发了一个新的深度学习框架，使用组织病理学图像对乳腺癌亚型进行分类，有可能减少对昂贵分子检测的需求。该方法采用多目标斑块选择策略，结合遗传算法和不确定性估计，以识别用于分类的信息性图像斑块。该方法在内部和外部数据集上均取得了较高的F1分数和AUC值，证明了其通过提供计算效率高、基于成像的替代方案来支持临床决策的潜力。
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SIMPLER framework uses H&E staining to improve SIM microscopy image analysis
Researchers have developed SIMPLER, a novel self-supervised pretraining framework designed to improve representation learning for Structured Illumination Microscopy (SIM) by leveraging Hematoxylin and Eosin (H&E) staine…
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Foundation models enable weakly supervised Nancy Index scoring for ulcerative colitis
Researchers have developed a weakly supervised multiple instance learning approach for automated scoring of ulcerative colitis activity using foundation models. This method leverages case- and slide-level labels to pred…
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AI models enable whole-cell segmentation in histology images
Researchers have developed two novel AI approaches for histopathology image analysis. One method, VitaminP, uses cross-modal learning to enable whole-cell segmentation from standard H&E stained images by transferring in…