 However, genomic testing  lacks clinical penetration in low resource settings and may not be available at scale for every patient. Here we show that deep learning-based weakly supervised and multi-task computational pathology on H E slides can be used to determine the metastatic status and site of origin for tumors of unknown primary .
<coherence>  However, genomic testing  lacks clinical penetration in low resource settings and may not be available at scale for every patient. Here we show that deep learning-based weakly supervised and multi-task computational pathology on H E slides can be used to determine the metastatic status and site of origin for tumors of unknown primary .
 However, genomic testing lacks clinical penetration in low resource settings . Herein, to overcome these challenges, we present a deep learning-based weakly supervised and multi-task computational pathology on H E slides can be used to determine the metastatic status and site of origin for tumors of unknown primary .