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 …

Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …

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 …

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …

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 …

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

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

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 …

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 …