LOGICA adds context to pretrained biological LMs via logit-space contrastive alignment with gated adapters, improving AUC on held-out drug-resistance mutation ranking from ~0.55 to ~0.65 while preserving token likelihoods.
Cell Reports 23, 181–193
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SegTME-UNI2 pairs a UNI2-based dual-head segmentation model trained via progressive pseudo-labeling with an LLM to produce multiclass cell maps and narrative TME descriptions from H&E images.
citing papers explorer
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Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment
LOGICA adds context to pretrained biological LMs via logit-space contrastive alignment with gated adapters, improving AUC on held-out drug-resistance mutation ranking from ~0.55 to ~0.65 while preserving token likelihoods.
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SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
SegTME-UNI2 pairs a UNI2-based dual-head segmentation model trained via progressive pseudo-labeling with an LLM to produce multiclass cell maps and narrative TME descriptions from H&E images.