Amin Naghdloo

This paper introduces an automated method to detect rare events in liquid biopsies without prior knowledge of their signatures, demonstrating its effectiveness on immunofluorescence microscopy data.

This paper introduces a contrastive learning framework for single-cell phenotyping in whole slide imaging of liquid biopsy samples, achieving high accuracy and enabling automated identification of rare cell phenotypes.

The paper identifies polyploid cancer cells as markers of chemotherapy resistance and recurrence in prostate cancer, linking them to poorer survival outcomes.