arXiv Machine Learning
CytoCrowd: A Multi-Annotator Benchmark Dataset for Cytology Image Analysis
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CytoSPM: Open-Vocabulary Cytopathology Detection with Structured Prompt Bank
arXiv:2609.31314v1 Announce Type: new Abstract: Cytopathology detection requires open-vocabulary recognition because cellular categories are fine-grained, long-tailed, and continuously evolving acros...
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Refining Cytology Predictions with Conditional Random Fields
The paper introduces CytoCRF, a conditional random field framework tailored for cytology images. It adapts pairwise terms to focus on chromatin and cytology-specific staining and enriches neighborhood information by combining multiple backbone models. Across ten cytology datasets, CytoCRF surpasses existing CRF methods at all annotation budgets, achieving up to +13.6 percentage points over the best baseline and +33.7 over zero‑shot performance with only 50 annotations.
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MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
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