Add to ORKGThe deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks. While this paradigm shift has become common practice within the field of natural language processing, progress has been slower in computer vision. In this paper we attempt to address this issue by investigating the transferability of various state-of-the-art foundation models to medical image classification tasks. Specifically, we evaluate the performance of five foundation models, namely SAM, SEEM, DINOv2, BLIP, and OpenCLIP across four well-established medical imaging datasets. We explore different training settings to fully harness the potential of these models. Our study shows mixed results. DINOv2 consistentl...
Self-supervised pre-training has become the priory choice to establish reliable models for automated...
Recent advances in vision and language (V+L) models have a promising impact in the healthcare field....
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image cl...
Multi-modal foundation models are typically trained on millions of pairs of natural images and text ...
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capab...
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learni...
Foundation models represent a recent paradigm shift in deep learning, where a single large-scale mod...
One of the main challenges of employing deep learning models in the field of medicine is a lack of t...
Transfer learning is a standard technique to transfer knowledge from one domain to another. For appl...
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated sample...
While a key component to the success of deep learning is the availability of massive amounts of trai...
The application of Convolutional Neural Networks (CNNs) for medical image classification and segment...
peer reviewedIn this work, we investigate multi-task learning as a way of pre-training models for cl...
Crafting effective deep learning models for medical image analysis is a complex task, particularly i...
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficien...
Self-supervised pre-training has become the priory choice to establish reliable models for automated...
Recent advances in vision and language (V+L) models have a promising impact in the healthcare field....
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image cl...
Multi-modal foundation models are typically trained on millions of pairs of natural images and text ...
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capab...
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learni...
Foundation models represent a recent paradigm shift in deep learning, where a single large-scale mod...
One of the main challenges of employing deep learning models in the field of medicine is a lack of t...
Transfer learning is a standard technique to transfer knowledge from one domain to another. For appl...
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated sample...
While a key component to the success of deep learning is the availability of massive amounts of trai...
The application of Convolutional Neural Networks (CNNs) for medical image classification and segment...
peer reviewedIn this work, we investigate multi-task learning as a way of pre-training models for cl...
Crafting effective deep learning models for medical image analysis is a complex task, particularly i...
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficien...
Self-supervised pre-training has become the priory choice to establish reliable models for automated...
Recent advances in vision and language (V+L) models have a promising impact in the healthcare field....
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image cl...