Goto

Collaborating Authors

Have you heard of optoacoustic imaging? This is how it's saving lives

#artificialintelligence

Optoacoustics is similar in some respects to ultrasound imaging. In the latter, a probe sends ultrasonic waves into the body, which the tissue reflects. Sensors in the probe detect the returning sound waves and generate a picture of the inside of the body. Optoacoustic imaging instead sends very short laser pulses into the tissue, where they're absorbed and converted into ultrasonic waves. Similarly to ultrasound imaging, researchers can then detect the waves convert them into images.


Survey: Majority of Imaging Leaders See Important Role for Machine Learning in Radiology

#artificialintelligence

While there is much hype around machine learning and its uses in healthcare, a recent survey indicates that machine learning is not just a buzzword, as 84 percent of medical imaging professionals view the technology as being either important or extremely important in medical imaging. What's more, about 20 percent of medical imaging professionals say they have already adopted machine learning, and about one-third say they will adopt it by 2020.


Advances and New Insights into Cancer Characterization: When Novel Imaging Meets Quantitative Imaging Biomarkers

#artificialintelligence

Computational medical imaging approaches can improve the analytical accuracy of interpretation in cancer identification and characterization, allowing for earlier disease detection and a better understanding of physiology and pathology. Machine Learning (ML) models have revolutionized many activities of medical imaging applications, such as novel imaging techniques, segmentation, registration, and synthesis, by analyzing large amounts of quantitative imaging biomarkers. While ML models outperform traditional methods on these tasks, they are still largely tacit in terms of explaining the data under consideration.This has reduced the interpretability of ML models, which is one of the major obstacles to ML-based pathology identification and generalized single- or multi-modal and multi-scale interpretation in medical imaging. Detailed examples of model behaviors are expected in current clinical practices to promote reliability and improve clinical decision making. Furthermore, the primary challenge for designing explainable models is to provide rationales while retaining high learning results as one of the most exciting areas of medical imaging science.We hope to attract novel, high-quality research and survey papers that represent the most recent developments in ML models in innovative medical imaging (MRI, CT, PET, SPECT, Ultrasound, histology and others) modalities or multi-modalities, by investigating novel methodologies either by interpreting algorithm components or by ex...


QUIBIM โ€“ QUantitative Imaging Biomarkers In Medicine

#artificialintelligence

We assist in the full process of imaging biomarkers integration, from the optimization of the images acquisition in the equipments to the imaging biomarkers report integration into the PACS. The platform can be either installed locally or used as a service in the Cloud, thanks to our interoperability. Our platform allows to centrally manage, store and quantitatively analyze the images, providing the'real time clinical trial' concept. The platform allows to select the best patients and to early evaluate treatment response. Imaging Biomarkers provided by QUIBIM should be also defined "radiomics" because they will be an essential component of the so called "systems medicine" and "omics".


BLOG: Artificial Intelligence May Help Enterprise Imaging

#artificialintelligence

After languishing for years, enterprise imaging appears ready to enter the mainstream of health care. AI Hot Spots: Where Is Artificial Intelligence Heading Now? โ€“ InformationWeek