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This wiki is intended to be used as a reference for the concepts and instructions of the project. If you are new for the project and Framework, it is important that you follow this wiki with attention. Although it is an important piece of information documentation, this wiki is not supposed to replace the official documentation of each tool, prototype or used library.
MIMBCD-UI Project deals with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). These deep networks will incorporate information from several modes: magnetic resonance imaging volumes (MRI), ultrasound images, mammographic images (both views CC and MLO) and text.
The proposed algorithm, called for multimodality CNNs (MMCNNs) will have the ability to process multimodal information at a unified and sustained manner. This methodology needs to "learn" what are the masses and calcifications. So that is necessary to collect the ground-truth or notes of the masses and calcifications provided by medical experts.
For the collection of these notes, the design and development of an interface is necessary to allow users (in this case, medical specialists) to display various types of image (i.e., ultrasound, MRI and mammography), and that also allows for user interaction, particularly in providing the notes of the masses and calcifications. For these reasons, it is crucial for the development of this project, cooperation with experts providing the above notes.
Medical imaging diagnosis is a routine effort performed by radiologists to help diagnose or monitor a patient's medical condition. Medical imaging diagnosis allows physicians to identify pathologies by decoding tissue characteristics while examining visual properties in medical imaging. It plays a central role in modern medicine, particularly in the prevention and diagnosis of breast cancer, one of the leading causes of mortality worldwide. Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012. However, proper classification, location, detection, targeting and registration of tumors requires the use of different imaging modalities that contribute to diagnostic reliability.
With the hype of new Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) algorithms, automated agents (assisted by AI) are closer to the radiology room than ever before. This proximity becomes highly relevant for the medical community, which is interested in solutions to support clinical diagnosis. Greater stakeholder engagement requires sufficient validation and continuously monitored emerging AI tools as well as available breast cancer screening data. That said, national health systems are increasingly in need of methods, such as our solutions, for ongoing clinical diagnostic support to support medical decision-making.