FAQ
Our Frequently Asked Questions (FAQ) are that nifty sections of our projects where we will find answers to recurring the project questions. For our BreastScreening (FAQ), MIDA (FAQ) and MIMBCD-UI (FAQ) projects, they guide us to perform common actions, such as typical configurations or fixing some issues. Please, follow the respective FAQ for each project, as it may have different Questions/Answers. We also provide a private wiki (FAQ) on the meta-private repository for team usage. Unfortunately, you need to be a member of our team to access the restricted information.
The purpose of this section is to provide answers regarding several aspects of the MIMBCD-UI project. A project that is highly contribution for both BreastScreening and MIDA projects. The project is part of the work done between SIPg, an ISR-Lisboa group, INESC-ID and ITI. ISR-Lisboa and INESC-ID are both Associate Laboratories from IST at ULisboa. ISR-Lisboa and ITI are both R&D Units of LARSyS.
Below, we will find answers to all of your medical imaging questions. Bookmark this page and check back often, as it is updated with new questions.
In short, medical imaging is a vast and complex area, with a bunch of information. In the MIMBCD-UI project we just approach the breast cancer domain, therefore we will just focus on MI for a breast cancer perspective. On the BreastScreening project FAQ page we address this topic. Please be free to follow your questions/answers there regarding the medical imaging issues.
There are many available contents concerning where to start, but if you are here on this platform, the best way is to start by the deep-learning-drizzle repository. Additional to that, you have a great a great MOOC course, namely "AI for Medicine Specialization", on Coursera offered by DeepLearning.AI.
For the project purpose, we are using both CornerstoneJS and Orthanc to support our medical imaging interactions. On one hand, CornerstoneJS provides an easy way to build interactive medical imaging web applications. On the other hand, Orthanc in an Open Source, lightweight DICOM server for medical imaging technologies.
The DICOM format stands for Digital Imaging and Communications in Medicine (DICOM) and is the standard for the communication and management of medical imaging information and related data. The mission of the DICOM format is to ensure the interoperability of systems used to manipulate medical images, as well as to manage related workflows. A good point to start is the article DICOM File Format Basics where it describes how does it work.
Several competitors are listed by the Food and Drug Administration (FDA) page, namely "Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices", where a full list of AI/ML devices can be found here. The FDA is a U.S.A. organization responsible for protecting the public health by ensuring the safety, efficacy, and security of human and veterinary drugs, biological products, and medical devices; and by ensuring the safety of our nation's food supply, cosmetics, and products that emit radiation.
List of potential partners:
In medical imaging, the growth of the market will be driven primarily by increasing demand for cost-effective solutions. The value of the medical imaging sector is expected to swell to $21.14 billion by 2027, according to data from Polaris Market Research.
Our platform is assembled by a set of tools. Some tools are complex by nature and may bring some questions together. Those questions should be answered and, therefore, we will answer them here regarding the platform related topics.
During the time, we are doing several questions by our selves. Some of these questions are presented on the CornerstoneJS Google Forum. To answer this question, we did the same question on the forum. For the answer, it is also important to follow this issue.
Wikipedia is an important tool to advertise our work. In this tool, we will be able to insert our respective publications and links, so that people can follow our work. The following sections will address this topic, as well as demonstrating several templates to do so.
The following code is showing a template, which is used to create citations for published conference proceedings on Wikipedia. For instance, we used this template on the "Computer-aided diagnosis" article.
The template on a multiline and vertical format:
<ref>
{{cite conference
| url = https://dl.acm.org/citation.cfm?id=3134111
| title = Towards Touch-Based Medical Image Diagnosis Annotation
| last1 = Calisto
| first1 = Francisco Maria
| last2 = Ferreira
| first2 = Alfredo
| last3 = C. Nascimento
| first3 = Jacinto
| last4 = Gonçalves
| first4 = Daniel
| date = 17 October 2017
| publisher = ACM
| pages = 390-395
| location = Brighton, United Kingdom
| conference = ISS '17 Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces
| isbn = 978-1-4503-4691-7
| doi = 10.1145/3132272.3134111
}}
</ref>
Or in a single and horizontal format:
<ref>{{cite conference | url = https://dl.acm.org/citation.cfm?id=3134111 | title = Towards Touch-Based Medical Image Diagnosis Annotation | last1 = Calisto | first1 = Francisco Maria | last2 = Ferreira | first2 = Alfredo | last3 = C. Nascimento | first3 = Jacinto | last4 = Gonçalves | first4 = Daniel | date = 17 October 2017 | publisher = ACM | pages = 390-395 | location = Brighton, United Kingdom | conference = ISS '17 Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces | isbn = 978-1-4503-4691-7 | doi = 10.1145/3132272.3134111 }}</ref>
Companies can support academia research by helping to introduce meaningful innovations into real clinical scenarios. These companies can provide technology transfer options for integrating innovation in the area of health and well-being, targeted at both developed and emerging markets. Positioned at the front of the innovation process, business solutions can work on everything, from spotting trends and ideation to proof-of-concept and product development. Our project, not only provides research challenges to the contributors of the project, but also important knowledge to the medical imaging market. Hence, several companies can provide an important opportunity to integrate the achieved knowledge into the market, so that your research can solve real problems.
Completing your research studies may be a great thing you have achieved in your path. It is so all-encompassing. Indeed, you might find yourself wrestling with one surprising question when you are finally done: 'what do I do with myself now?' While you will eventually join the workforce in academia, industry, or government, if you find yourself in this position, here are several opportunities you can do while you move toward whatever is coming next.
A list of the top radiology and medical imaging startups can be found on the MedicalStartups page with the title "Top 51 Radiology and medical imaging startups", where radiology startups develop new mostly AI-based technologies to automate detecting and diagnosis of cancer. Moreover, another look at the top AI-powered radiology startups to keep an eye on in 2021, shows the "Top 10 AI-powered Radiology Startups to Watch Out for in 2021" by IndustryWired.
The following list of interesting companies arise:
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Microsoft Research [ Project InnerEye | Health Intelligence ]
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Fruto Studio [ LinkedIn ]
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Kheiron Medical [ LinkedIn | Twitter ]
- Therapixel [ LinkedIn ]
People to follow if you want to be aware of Human-AI interaction works: