Unusual file formats within your day-to-day papers management and modifying operations can create instant confusion over how to edit them. You may need more than pre-installed computer software for efficient and fast document modifying. If you want to negate image in docbook or make any other basic alternation in your document, choose a document editor that has the features for you to deal with ease. To handle all the formats, such as docbook, choosing an editor that works well with all types of documents is your best choice.
Try DocHub for effective document management, regardless of your document’s format. It has powerful online editing tools that simplify your papers management operations. It is easy to create, edit, annotate, and share any document, as all you need to access these features is an internet connection and an functioning DocHub profile. Just one document solution is all you need. Don’t lose time jumping between various programs for different documents.
Enjoy the efficiency of working with a tool created specifically to simplify papers processing. See how effortless it is to edit any document, even when it is the very first time you have worked with its format. Sign up an account now and enhance your entire working process.
Hello everybody, and welcome. My name is Dennis. Im a research scholar at the Harvard Artificial Intelligence in Medicine program, or AIM, participating in IDC to explore the integration of AI-based imaging analysis pipelines in the platform. In the previous videos, we showed how to use the IDC portal to build cohorts, how to access the files corresponding to a cohort, and how to build customized dashboards for your cohorts. In this brief video, we will show you one of the use cases we developed so far, and explain to you how the IDC cloud infrastructure can be leveraged to do imaging research as well as what are the advantages the platform brings to the end user. This initial use case we developed is a replication and extension of the study published on PLOS medicine by Hosny and colleagues, where the authors compared the prognostic power of Deep Learning convolutional neural networks to radiomics models, in an imaging cohort of non-small-cell lung cancer