Releases Pollen AI Atlas, a million-scale multimodal pollen microscopy dataset with expert-guided VLM captions and baseline benchmarks for recognition and cross-regional retrieval.
Lu, Bowen Chen, Drew F
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SIMPLER learns biologically grounded SIM representations by progressively aligning them with H&E images through multiple self-supervised objectives, outperforming scratch-trained or H&E-only models on downstream tasks like multiple instance learning and clustering.
QG-MIL introduces four gated transformer components that yield +6.1 average macro F1 improvement over baselines on six whole-slide and cell-level medical imaging benchmarks while producing more uniform attention.
Retrieval-guided captioning from similar cases achieves higher semantic alignment (cosine similarity ~0.60 vs ~0.47) and fewer unsupported diagnoses than MedGemma on the ARCH dataset.
CLEAR-HPV restructures the latent space of attention-based MIL models to discover 10 label-free morphologic concepts that preserve slide-level HPV prediction performance and generalize across TCGA-HNSCC, TCGA-CESC, and CPTAC-HNSCC datasets.
Vibe Medicine proposes directing AI agents via natural language for end-to-end biomedical workflows using LLMs, agent frameworks, and a curated collection of over 1,000 medical skills.
citing papers explorer
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Million-scale multimodal pollen microscopy with expert-guided foundation models
Releases Pollen AI Atlas, a million-scale multimodal pollen microscopy dataset with expert-guided VLM captions and baseline benchmarks for recognition and cross-regional retrieval.
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SIMPLER: H&E-Informed Representation Learning for Structured Illumination Microscopy
SIMPLER learns biologically grounded SIM representations by progressively aligning them with H&E images through multiple self-supervised objectives, outperforming scratch-trained or H&E-only models on downstream tasks like multiple instance learning and clustering.
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QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging
QG-MIL introduces four gated transformer components that yield +6.1 average macro F1 improvement over baselines on six whole-slide and cell-level medical imaging benchmarks while producing more uniform attention.
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Retrieval-Guided Generation for Safer Histopathology Image Captioning
Retrieval-guided captioning from similar cases achieves higher semantic alignment (cosine similarity ~0.60 vs ~0.47) and fewer unsupported diagnoses than MedGemma on the ARCH dataset.
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CLEAR-HPV: Interpretable concept discovery for human-papillomavirus-associated morphology in whole-slide histology
CLEAR-HPV restructures the latent space of attention-based MIL models to discover 10 label-free morphologic concepts that preserve slide-level HPV prediction performance and generalize across TCGA-HNSCC, TCGA-CESC, and CPTAC-HNSCC datasets.
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Vibe Medicine: Redefining Biomedical Research Through Human-AI Co-Work
Vibe Medicine proposes directing AI agents via natural language for end-to-end biomedical workflows using LLMs, agent frameworks, and a curated collection of over 1,000 medical skills.