In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become�…

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional�…

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We�…

Convolutional neural networks (CNNs) have so far been the de-facto model for visual data.
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or�…

Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream�…

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications�…

The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt�…

In tasks involving the interpretation of medical images, suitably trained machine-learning
models often exceed the performance of medical experts. Yet such a high-level of�…

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby�…

Self-supervised pretraining followed by supervised fine-tuning has seen success in image
recognition, especially when labeled examples are scarce, but has received limited attention�…