PathRTM: Real-time prediction of KI-67 and tumor-infiltrated lymphocytes
This work addresses cancer diagnosis and treatment by improving estimation accuracy and annotation efficiency, though it is incremental as it extends prior work like PathoNet.
The paper tackles automated KI-67 proliferation and tumor-infiltrated lymphocyte estimation in cancer diagnosis by introducing PathRTM, a deep neural network detector that achieves state-of-the-art performance with an average precision of 41.3% on a custom dataset.
In this paper, we introduce PathRTM, a novel deep neural network detector based on RTMDet, for automated KI-67 proliferation and tumor-infiltrated lymphocyte estimation. KI-67 proliferation and tumor-infiltrated lymphocyte estimation play a crucial role in cancer diagnosis and treatment. PathRTM is an extension of the PathoNet work, which uses single pixel keypoints for within each cell. We demonstrate that PathRTM, with higher-level supervision in the form of bounding box labels generated automatically from the keypoints using NuClick, can significantly improve KI-67 proliferation and tumorinfiltrated lymphocyte estimation. Experiments on our custom dataset show that PathRTM achieves state-of-the-art performance in KI-67 immunopositive, immunonegative, and lymphocyte detection, with an average precision (AP) of 41.3%. Our results suggest that PathRTM is a promising approach for accurate KI-67 proliferation and tumor-infiltrated lymphocyte estimation, offering annotation efficiency, accurate predictive capabilities, and improved runtime. The method also enables estimation of cell sizes of interest, which was previously unavailable, through the bounding box predictions.