Search Results for author:
Found 6 papers, 2 papers with code
Exploring the Boundaries of GPT-4 in Radiology
In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models.
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs.
Ranked #3 on
Medical Visual Question Answering
on SLAKE-English
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Results: We conduct an extensive study on 135, 107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states.
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Overall, domainspecific vocabulary and pretraining facilitate more robust models for fine-tuning.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
Ranked #2 on
Participant Intervention Comparison Outcome Extraction
on EBM-NLP
(using extra training data)
