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Found 12 papers, 7 papers with code
BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors
Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models.
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
We demonstrate video pretraining improves the average performance of seven 3D models on two chest CT datasets, regardless of finetuning dataset size, and that video pretraining allows 3D models to outperform 2D baselines.
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundamental tasks of medical image analysis.
Diagnosing and Rectifying Vision Models using Language
Our proposed method can discover high-error data slices, identify influential attributes and further rectify undesirable model behaviors, without requiring any visual data.
General Framework for Self-Supervised Model Priming for Parameter-Efficient Fine-tuning
To tackle these issues, we propose a general framework to enhance the few-shot adaptation and cross-domain generalization ability of parameter-efficient methods.
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy.
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition
In recent years, the growing number of medical imaging studies is placing an ever-increasing burden on radiologists.
Ten Simple Rules for Reproducible Research in Jupyter Notebooks
For example, what are the technical and non-technical barriers to reproducible computational studies?
Other Computer Science Computers and Society
