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In this paper, we introduce PathRTM, a novel deep neural network detectorbased on RTMDet, for automated KI-67 proliferation and tumor-infiltratedlymphocyte estimation. KI-67 proliferation and tumor-infiltrated lymphocyteestimation play a crucial role in cancer diagnosis and treatment. PathRTM is anextension of the PathoNet work, which uses single pixel keypoints for withineach cell. We demonstrate that PathRTM, with higher-level supervision in theform of bounding box labels generated automatically from the keypoints usingNuClick, can significantly improve KI-67 proliferation and tumorinfiltratedlymphocyte estimation. Experiments on our custom dataset show that PathRTMachieves state-of-the-art performance in KI-67 immunopositive, immunonegative,and lymphocyte detection, with an average precision (AP) of 41.3%. Our resultssuggest that PathRTM is a promising approach for accurate KI-67 proliferationand tumor-infiltrated lymphocyte estimation, offering annotation efficiency,accurate predictive capabilities, and improved runtime. The method also enablesestimation of cell sizes of interest, which was previously unavailable, throughthe bounding box predictions.

PathRTM: Real-time prediction of KI-67 and tumor-infiltrated lymphocytes