Mohammed, Mohammed AliyMohammed AliyMohammedJonkers, JefJefJonkersKrishnamoorthy, JanarthananJanarthananKrishnamoorthyDe Neve, WesleyWesleyDe NeveVan Hoecke, SofieSofieVan Hoecke2026-09-162026-09-162027978-3-032-30614-21868-4238https://imec-publications.be/handle/20.500.12860/60382Deep learning object detectors in digital parasitology typically rely on heuristic confidence scores and routinely omit rigorous uncertainty quantification, limiting their trustworthiness for clinical deployment. We introduce a dual-head detector combined with a conformal risk control (CRC) framework to produce effective and risk-controlled predictions with formal, finite-sample risk guarantees. By sequentially conformalizing confidence thresholding, localization, and classification, the pipeline ensures that outputs adhere to a user-specified deployment risk level. Evaluated on a four-class and an 11-class stool parasite egg image dataset, the CRC framework maintains valid risk control, prunes over 92–99% of background proposals, and achieves marginal coverage within finite-sample deviations, with zero spatial expansion under moderate and relaxed requirements. Qualitative analysis confirms that the pipeline adaptively modulates the cardinality of the prediction set in response to instance-level difficulty. Further, an open-set edge case, created by withholding one parasite class during training, shows that CRC suppresses nearly all unknown-class instances, with the rare survivors receiving full prediction sets that explicitly flag uncertainty. By transforming heuristic point estimates into risk-bounded inference, our approach provides a tunable, mathematically rigorous foundation for reliable, deployable AI-assisted microscopic diagnosis of parasitic eggs.engProceedings paper10.1007/978-3-032-30612-8_25WOS:001853368300025