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    Models/Image Feature Extraction/ratschlab/DeepSpotM
    Image Feature Extractiondeepspotmcc-by-nc-sa-4.0

    DeepSpotM

    by ratschlab

    Identifier
    Model ID
    ratschlab/DeepSpotM

    Tags

    deepspotmsafetensorsbiologymedicalhistologypathologyspatial-transcriptomicsgene-expressionloracomputational-pathologyfoundation-modelmultimodalvirtual-spatial-transcriptomicswhole-slide-imagingoncologytranscriptomicsdeep-learningcancerimage-feature-extractionendataset:ratschlab/TCGA_virtual_spatial_transcriptomics_atlasdataset:ratschlab/HEST_Xenium_virtual_spatial_transcriptomicsbase_model:kaiko-ai/midnightbase_model:adapter:kaiko-ai/midnightlicense:cc-by-nc-sa-4.0region:us

    Use DeepSpotM on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

    Open Studio

    How It Runs on Mixpeek

    On Mixpeek, DeepSpotM runs as a managed extractor inside a processing pipeline. Point a bucket of image feature extraction data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.