The composition of the tumor immune microenvironment (TME) is complex and challenging to quantify manually.
• Machine learning (ML) algorithms can be used to characterize the spatial distribution of cells and tissue regions of the TME from digitized H&E stained whole slide images (WSI) of multiple cancer types.
• Based on ML-based TME characterization, we extracted TME-associated human interpretable features (HIFs) to generate an atlas characterizing the TME in several cancer types, including bladder cancer, breast cancer, and non-small cell lung cancer (NSCLC). We term this atlas Tumor Immune Microenvironment Atlas Project (TIMAP). Conway et al.
• Machine learning (ML) algorithms can be used to characterize the spatial distribution of cells and tissue regions of the TME from digitized H&E stained whole slide images (WSI) of multiple cancer types.
• Based on ML-based TME characterization, we extracted TME-associated human interpretable features (HIFs) to generate an atlas characterizing the TME in several cancer types, including bladder cancer, breast cancer, and non-small cell lung cancer (NSCLC). We term this atlas Tumor Immune Microenvironment Atlas Project (TIMAP). Conway et al.
