arXiv Computer Vision
Label-free cell counting and viability prediction with brightfield imaging and deep learning
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arXiv AI
Jul 28
scMIR: a vision-language foundation model for single-cell light microscopy image representation
arXiv:2607. 22712v1 Announce Type: cross Abstract: Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis.
arXiv Computer Vision
Oct 12
Detection of extremely rare dead cells in fluorescence microscopy images
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.
arXiv AI
Jun 3
CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations
arXiv:2606. 03435v1 Announce Type: new Abstract: Cell Painting combines multiplexed fluorescent staining, high-content imaging, and quantitative analysis to generate high-dimensional phenotypic readouts to support diverse downstream tasks such as mechanism-of-action (MoA) inference, toxicity prediction, and construction of drug-disease atlases.
arXiv AI
Jul 1
Toxicity Assessment in Preclinical Histopathology via Class-Aware Mahalanobis Distance for Known and Novel Anomalies
arXiv:2602. 02124v2 Announce Type: replace-cross Abstract: Drug-induced toxicity is a leading cause of preclinical and early-clinical failure, making early detection critical.
arXiv AI
Jul 2
MalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
arXiv:2607. 00385v1 Announce Type: cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical challenge in global health AI; in resource-limited settings, the scarcity of expert microscopists remains the primary bottleneck to timely and accurate diagnosis.
arXiv AI
Aug 5
MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
arXiv:2607. 00385v2 Announce Type: replace-cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical global health AI challenge; expert scarcity remains the primary diagnostic bottleneck.