In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline
patient diagnosis and management has become more pressing than ever. As one of the …

Radiology report generation aims to automatically generate a clinically accurate and
coherent paragraph from the X-ray image, which could relieve radiologists from the heavy …

Multimodal models trained on large natural image-text pair datasets have exhibited
astounding abilities in generating high-quality images. Medical imaging data is …

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next
decades. Specifically, AI systems leveraging multiple data sources and input modalities are …

Methods Images from a public COVID-19 database were scored retrospectively by three
blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A …

This large scale study focuses on quantifying what X-rays diagnostic prediction tasks
generalize well across multiple different datasets. We present evidence that the issue of …

The proposed model for automatic clinical image caption generation combines the analysis
of radiological scans with structured patient information from the textual records. It uses two …

Training deep learning models on medical images heavily depends on experts' expensive
and laborious manual labels. In addition, these images, labels, and even models …

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the
gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important …