Image classification and segmentation are common applications of deep learning to
radiology. While many tasks can be framed using either classification or segmentation …

Objective Weak supervision holds significant promise to improve clinical natural language
processing by leveraging domain resources and expertise instead of large manually …

Objectives Radiology reports contain important clinical information that can be used to
automatically construct fine-grained labels for applications requiring deep phenoty**. We …

In medical imaging, there is a clash between the appealing simplicity of automation and the
complex demands of human judgement. This tension gets explored, especially when …

Many clinical informatics tasks that are based on electronic health records (EHR) need
relevant patient cohorts to be selected based on findings, symptoms and diseases …

With the emergence of large-scale vision-language models, realistic radiology reports may
be generated using only medical images as input guided by simple prompts. However, their …

Several evaluation metrics have been developed recently to automatically assess the quality
of generative AI reports for chest radiographs based only on textual information using …

Mechanisms are provided to implement an automated medi cal imaging report generator
which receives an input medi cal image and inputs the input medical image into a machine …

Medical imaging is an essential tool in healthcare, and radiologists are highly trained to
detect and characterize disease in medical images. However, relying solely on human …

Chest X-rays have become the focus of vigorous deep learning research in recent years due
to the availability of large labeled datasets. While classification of anomalous findings is now …