This work illustrates how tumor proportional score is estimated using object detection method YOLO and compared with a pathologist's calculation. Results show deep learning can achieve good results and be used on clinical applications.
The lowest achievable blocking temperature limits magnetic ordering in highly frustrated thermally active artificial kagome spin ice. By exploiting the interfacial Dzyaloshinskii-Moriya interaction, we can lower the blocking temperature of individual nanomagnets without strongly affecting their magnetic moments, thus leaving the critical transition temperatures unchanged. Using this approach, we demonstrate that a seven-ring kagome structure consisting of 30 nanomagnets can be thermally annealed into its ground state. Furthermore, the spin-ice correlations extracted from extended kagome lattices are found to exhibit the quantitative signatures of long-range charge-order, thereby giving experimental evidence for the theoretically predicted continuous transition to a charge-ordered state.
At the present, identifying head and neck squamous cell carcinoma (HNSCC) patients for immune checkpoint inhibitor therapy (ICIT) is achieved through the determination of Tumor Proportion Score (TPS) or the percentage of tumor cells positively labeled for PD-L1. Estimation of TPS is largely done in a manual fashion by a trained pathologist. In the case of HNSCC, the histological section can be over 1 cm in size in which over 100,000 cancer cells need to be evaluated for PD-L1 expression. To expedite the TPS evaluation process for such large specimens, we have developed a platform in which artificial intelligence (AI) is used for TPS determination. With additional development, this approach may be used in the clinical setting to assist pathologists in TPS evaluation.
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