病理人工智能前沿文献精选（第二期）2026.05.24
《病理AI前沿周报》第二期（2026年5月24日）。我们将于每个周末为您推送，快速了解病理人工智能领域的最新动态，期待您的关注！本次为您盘点了8项近期备受瞩目的病理AI前沿成果，研究领域涵盖：免疫组化（IHC）细胞定量分析、肝细胞癌血管形态定量、肾脏空间糖组学探索、肿瘤免疫微环境评估、胃癌淋巴结微转移预测、乳腺癌肿瘤浸润淋巴细胞（TIL）标准化分析、膀胱癌免疫表型鉴定，以及基于H&E染色图像预测空间转录组数据等。 1. HESpotEx: a dual-stream deep learning framework for spot-level gene expression prediction from histological images, Nature Computational Science 2. Rank-aware agglomeration of foundation models for immunohistochemistry image cell counting, Medical Image Analysis 3. Artificial intelligence-based vascular pattern profiling predicts prognosis and therapeutic response in hepatocellular carcinoma, Journal of Advanced Research 4. Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challenge, Nature Communications 5. Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study, British Journal of Cancer 6. Development and validation of artificial intelligence-based model for bladder cancer immunophenotyping using whole slide images, npj Precision Oncology 7. AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney, Journal of Proteome Research 8. Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies, Briefings in Bioinformatics 上述精选文献不仅包含解决实际临床痛点的AI算法，还涉及空间组学、多模态数据融合及标准化评估的方法学探索。大家更期待“AI病理视界”公众号深入解读哪一篇文章呢？欢迎在下方留言区分享您的看法！
每个周末准时更新，带您快速掌握病理AI最新科研动态，我们不见不散！本期汇总了8篇近期热度极高的病理AI学术论文，研究方向包括：免疫组化图像细胞计数、肝细胞癌血管网络特征提取、人类肾脏空间糖组学、肿瘤免疫浸润程度评价、胃癌淋巴结转移风险预测、乳腺癌TIL标准化评测体系、膀胱癌免疫表型识别，以及通过H&E图像预测空间转录组等。 1. HESpotEx: a dual-stream deep learning framework for spot-level gene expression prediction from histological images, Nature Computational Science 2. Rank-aware agglomeration of foundation models for immunohistochemistry image cell counting, Medical Image Analysis 3. Artificial intelligence-based vascular pattern profiling predicts prognosis and therapeutic response in hepatocellular carcinoma, Journal of Advanced Research 4. Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challenge, Nature Communications 5. Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study, British Journal of Cancer 6. Development and validation of artificial intelligence-based model for bladder cancer immunophenotyping using whole slide images, npj Precision Oncology 7. AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney, Journal of Proteome Research 8. Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies, Briefings in Bioinformatics 这些研究成果中，既有针对特定临床应用场景的AI模型构建，也有聚焦空间组学分析、多模态数据整合与标准化评测的方法论创新。您最希望我们在“AI病理视界”公众号中为您详细拆解哪一篇文献？欢迎在评论区留言互动！