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Found 11 papers, 1 papers with code
General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports
F1 score for extraction was 97% for RadLing-based system and 78% for GPT-4 system.
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient.
shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation
Instruction-tuned generative Large language models (LLMs) like ChatGPT and Bloomz possess excellent generalization abilities, but they face limitations in understanding radiology reports, particularly in the task of generating the IMPRESSIONS section from the FINDINGS section.
RadLing: Towards Efficient Radiology Report Understanding
Most natural language tasks in the radiology domain use language models pre-trained on biomedical corpus.
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report.
Assertion Detection in Multi-Label Clinical Text using Scope Localization
Multi-label sentences (text) in the clinical domain result from the rich description of scenarios during patient care.
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on
Natural Language Inference
on SNLI
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario.
Condensed Memory Networks for Clinical Diagnostic Inferencing
We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory.
Neural Clinical Paraphrase Generation with Attention
Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact.
Neural Paraphrase Generation with Stacked Residual LSTM Networks
To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.
