In this paper, we present UNet++, a new, more powerful architecture for medical image
segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network …

Large language models have been shown to achieve remarkable performance across a variety
of natural language tasks using few-shot learning, which drastically reduces the number …

The state-of-the-art models for medical image segmentation are variants of U-Net and fully
convolutional networks (FCN). Despite their success, these models have two limitations: (1) …

In response to innovations in machine learning (ML) models, production workloads changed
radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its …

Scaling language models with more data, compute and parameters has driven significant
progress in natural language processing. For example, thanks to scaling, GPT-3 was able to …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

An important security challenge is to protect the execution of security-sensitive code on legacy
systems from malware that may infect the OS, applications, or system devices. Prior work …

Intense interest in applying convolutional neural networks (CNNs) in biomedical image
analysis is wide spread, but its success is impeded by the lack of large annotated datasets in …

Google deployed several TPU generations since 2015, teaching us lessons that changed
our views: semi-conductor technology advances unequally; compiler compatibility trumps …

Transfer learning from natural image to medical image has established as one of the most
practical paradigms in deep learning for medical image analysis. However, to fit this paradigm…