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arXiv Computer Vision By Prateek Verma, Benjamin Butter, Jian Zhang, Xintao Wu

Detection of extremely rare dead cells in fluorescence microscopy images

Read the original on arXiv Computer Vision →

The study evaluates multiple computational methods for detecting dead cells in fluorescence microscopy images, comparing heuristic thresholding, classification with numerical and image features, and anomaly detection techniques. Using a dataset of 258 images and 65,527 cells, the best performance was achieved by deep learning image classifiers (ResNet-18) and traditional numerical classifiers (XGBoost), outperforming manual heuristics and anomaly detection. The results highlight the difficulty of reliably identifying apoptotic cells despite advanced machine learning approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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