CytoAL: Toward Label-Efficient Cytology Diagnosis via Cellularity-Guided Active Learning

Examining thyroid fine needle aspiration (FNA) can grade cancer risks, derive prognostic information, and guide follow-up care or surgery decision-making. However, thyroid cytology's diagnostic cues are more dispersed compared with pathology images in other disciplines, making standard annotation strategy for AI diagnosis labor-intensive. Inspired by how cytologists diagnose under the microscope, we propose an innovative cellularity-based active learning framework, namely Cyto-AL, to correlate cellularity with diagnostic categories for the active learning query. We also improve the Whole Slide Image (WSI) category of The Bethesda System for Reporting Thyroid Cytology (TBSRTC) prediction by proposing severe-stage pinpointed Multiple Instance Learning (MIL). Additionally, we introduce a lightweight score model to optimize the query in human in the loop (HITL) annotation strategy. Given scarce public thyroid cytology datasets, we release our collected and labeled images as benchmarks. The benchmark comprises 138 WSIs (27,496 valid image patches) collected from 2021-2023 across six classes, annotated by three pathologists using TBSRTC. At patch-level verification, Cyto-AL achieves a 2.2% average classification accuracy improvement over state-of-the-art methods with an equally labeled dataset, and its lightweight ranking-aware model reduces training time by around 65%. Moreover, the WSI-level MIL approach improves average accuracy by 10.7% and Macro-F1 score by 3.5%, outperforming standard sampling methods such as Monte Carlo sampling. The source code and dataset are available at https://github.com/Junchao-Zhu/Cyto-AL.