Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto approach, despite differences in the tasks and image characteristics between the domains. However, it is unclear what factors determine whether - and to what extent - transfer learning to the medical domain is useful. The long-standing assumption that features from the source domain get reused has recently been called into question. Through a series of experiments on several medical image benchmark datasets, we explore the relationship between transfer learning, data size, the capacity and inductive bias of the model, as well as the distance between the source and tar...
Transfer-learning has rapidly become one of the most sophisticated and effective techniques in deali...
Transfer learning is a widely used strategy in medical image analysis. Instead of only training a ne...
In deep learning, transfer learning (TL) has become the de facto approach when dealing with image re...
While a key component to the success of deep learning is the availability of massive amounts of trai...
One of the main challenges of employing deep learning models in the field of medicine is a lack of t...
Deep learning has achieved a great success in natural image classification. To overcome data-scarcit...
Multi-Stage Transfer Learning (MSTL) has been becoming a very promising area of research in the fiel...
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learni...
Transfer learning was successfully employed already at the very early rise of deep neural networks t...
Our work focuses on inductive transfer learning, a setting in which one assumes that both source and...
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficien...
In this work, we compare the performance of six state-of-the-art deep neural networks in classificat...
Transfer learning allows us to exploit knowledge gained from one task to assist in solving another b...
Background Transfer learning is a form of machine learning where a pre-trained model trained on a sp...
BackgroundTransfer learning is a form of machine learning where a pre-trained model trained on a spe...
Transfer-learning has rapidly become one of the most sophisticated and effective techniques in deali...
Transfer learning is a widely used strategy in medical image analysis. Instead of only training a ne...
In deep learning, transfer learning (TL) has become the de facto approach when dealing with image re...
While a key component to the success of deep learning is the availability of massive amounts of trai...
One of the main challenges of employing deep learning models in the field of medicine is a lack of t...
Deep learning has achieved a great success in natural image classification. To overcome data-scarcit...
Multi-Stage Transfer Learning (MSTL) has been becoming a very promising area of research in the fiel...
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learni...
Transfer learning was successfully employed already at the very early rise of deep neural networks t...
Our work focuses on inductive transfer learning, a setting in which one assumes that both source and...
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficien...
In this work, we compare the performance of six state-of-the-art deep neural networks in classificat...
Transfer learning allows us to exploit knowledge gained from one task to assist in solving another b...
Background Transfer learning is a form of machine learning where a pre-trained model trained on a sp...
BackgroundTransfer learning is a form of machine learning where a pre-trained model trained on a spe...
Transfer-learning has rapidly become one of the most sophisticated and effective techniques in deali...
Transfer learning is a widely used strategy in medical image analysis. Instead of only training a ne...
In deep learning, transfer learning (TL) has become the de facto approach when dealing with image re...