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arXiv Machine Learning By Kazuya Nishimura, Ryoma Bise, Haruka Hirose, Yasuhiro Kojima

Leveraging Cell-level Spatial Transcriptomics as Weak Supervision for Nuclei Classification

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The paper proposes a scalable framework that uses cell-level spatial transcriptomics (ST) as weak supervision for nuclei classification in histopathology images. By converting gene expression profiles into cell-type labels and leveraging nucleus masks from ST data, the method automatically generates image-label pairs without manual annotation. Evaluations on unseen tissues show that this approach achieves segmentation and classification performance comparable to conventional supervised methods while requiring fewer manually annotated datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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