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Nikita Moshkov

2 accepted papers

2026

CHAMMI-75: pre-training multi-channel models with heterogeneous microscopy images

ICLR 2026poster

Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditio…

Cited by 0SourcecodeScholar
2023

CHAMMI: A benchmark for channel-adaptive models in microscopy imaging

NeurIPS 2023poster

Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on instruments and experimental goals. Yet, there has…

Cited by 11SourcePDFScholar