Model weights and processed data - Cluster-based human-in-the-loop strategy for improving CTC detection and classification

The DINO model weights and the processed data accompany the following GitHub repository: CTC-HiL Detecting and differentiating circulating tumor cells (CTCs) in blood samples from cancer patients is challenging. Among the challenges is the reliance on manual annotation and evaluation. This study introduces a human-in-the-loop (HiL) approach to enhance ML-based CTC detection by combining self-supervised deep learning with a traditional classifier, using iterative sampling and human experts for efficient labeling based on classification performance in latent space clusters. Overview of what you can download from here: The DINO model ctc_dino.pt was trained on unlabeled single cell fluorescence images with three-channels (DAPI, CK, CD45) from 60 patients with metastatic breast cancer. The provided features and dataframes can be used to reproduce the simulation findings and the cluster plot. For more information, visit CTC-HiL.