PBS-VL trained on the new PBSInstr dataset outperforms general and pathology MLLMs on the PBSBench VQA tasks for hematopathology.
Ikezogwo, Mehmet S
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
representative citing papers
BiomedCLIP, pretrained on the new 15-million-pair PMC-15M dataset, achieves state-of-the-art performance on diverse biomedical vision-language tasks and even outperforms radiology-specific models on chest X-ray pneumonia detection.
GLMP generates robust pathology embeddings by routing histology images through an intermediate textual representation produced by general-purpose MLLMs to mitigate batch effects.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
citing papers explorer
-
PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation
PBS-VL trained on the new PBSInstr dataset outperforms general and pathology MLLMs on the PBSBench VQA tasks for hematopathology.
-
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
BiomedCLIP, pretrained on the new 15-million-pair PMC-15M dataset, achieves state-of-the-art performance on diverse biomedical vision-language tasks and even outperforms radiology-specific models on chest X-ray pneumonia detection.
-
Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
GLMP generates robust pathology embeddings by routing histology images through an intermediate textual representation produced by general-purpose MLLMs to mitigate batch effects.
-
Data-Centric Foundation Models in Computational Healthcare: A Survey
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.