Sayedali Shetab Boushehri

This paper introduces SUM-PAINT, a high-throughput imaging method for single-protein spatial resolution, and discovers a new synapse type through extensive multiprotein analysis.

scifAI provides an open-source framework for analyzing imaging flow cytometry data to predict antibody function and characterize immunological synapses.

The paper introduces a label-free deep learning method to predict nuclear staining in 3D stem cell cultures from bright-field images, enabling high-resolution tracking of differentiation dynamics.

PXPermute identifies important fluorescent channels in IFC data by permuting pixel values and evaluating model performance, showing superior accuracy in channel selection.

InstantDL provides a deep learning pipeline for biomedical image tasks with minimal effort, achieving competitive performance across seven datasets without parameter tuning.

The paper compares incomplete-supervision methods for biomedical image classification, finding that pre-training and semi-supervised learning contribute more than active learning to performance.