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06/15/2023
LOVM: Language-Only Vision Model Selection
Pre-trained multi-modal vision-language models (VLMs) are becoming incre...
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04/17/2023
BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors
Medical data poses a daunting challenge for AI algorithms: it exists in ...
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04/02/2023
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
Pretraining on large natural image classification datasets such as Image...
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02/08/2023
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 ...
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02/08/2023
Diagnosing and Rectifying Vision Models using Language
Recent multi-modal contrastive learning models have demonstrated the abi...
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12/02/2022
General Framework for Self-Supervised Model Priming for Parameter-Efficient Fine-tuning
Parameter-efficient methods (like Prompt or Adapters) for adapting pre-t...
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06/08/2022
Few-shot Prompting Towards Controllable Response Generation
Much literature has shown that prompt-based learning is an efficient met...
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11/23/2021
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 radiol...
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08/03/2021
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 mana...
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07/21/2021
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Background: In medical imaging, prior studies have demonstrated disparat...
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10/13/2018
