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Found 7 papers, 5 papers with code
Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the context of medical Large Language Models (LLMs).
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
Multi-modal fusion approaches aim to integrate information from different data sources.
Ranked #1 on
Phenotype classification
on MIMIC-CXR, MIMIC-IV
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data.
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
Here, we propose a multi-label generalized zero shot learning (CXR-ML-GZSL) network that can simultaneously predict multiple seen and unseen diseases in CXR images.
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study
In test set B (225 patient encounters), the respective system achieves 0. 90 AUROC for AKI, elevated troponin, and elevated interleukin-6, and >0. 80 AUROC for most of the other complications.
Synthesizing the Unseen for Zero-shot Object Detection
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference.
Ranked #1 on
Zero-Shot Object Detection
on ImageNet Detection
Towards Robust and Reproducible Active Learning Using Neural Networks
Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions.
Ranked #6 on
Active Learning
on CIFAR10 (10,000)
