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Found 3 papers, 1 papers with code
Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data
The pathological analysis of biopsy specimens is essential to cancer diagnosis, treatment selection and prognosis.
Weakly supervised pan-cancer segmentation tool
The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expert variability.
Weakly supervised multiple instance learning histopathological tumor segmentation
In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems.
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