Diagnostic Medicine

Compare outlier detection methods with the OutliersO3 package


There are many different methods for identifying outliers and a lot of them are available in R. But are outliers a matter of opinion? Articles on outlier methods use a mixture of theory and practice. Theory is all very well, but outliers are outliers because they don't follow theory. Practice involves testing methods on data, sometimes with data simulated based on theory, better with real' datasets.

Only Numpy Medical: Denosing Lung CT Scans using Neural Networks with Interactive Code -- Part 2…


Now lets take a look at the cost function that we are going to use. Since this equation set the value for us to optimize it is very important. Now again, please keep in mind the notation for this paper is mixed up LOL. But I can safely say the cost function we are going to use is L2 Norm divided by the number of batch size, rather then 0.5. This can be achieved by the code below.

AI expert: Marriage of machine learning, radiology may turn out different than you think


Machine learning and artificial intelligence (AI) are two hotly discussed topics in healthcare, but many radiologists tend to fear a future in which computers replace people.

Artificial intelligence can help analyze CT scans


Aidoc, a startup company pioneering the use of deep learning in radiology, announced on Wednesday the world's first comprehensive, full-body solution utilizing artificial intelligence to help analyze CT scans.

Radiology practices using AI and NLP to boost MIPS payments


Positive or negative Medicare payment adjustments in 2019 will depend on performance to quality and other measures in 2017 under a new program called the Merit-based Incentive Payment System. Doing well on quality measures is important because they comprise 60 percent of a provider's total MIPS score – possibly 85 percent for certain specialties such as radiology.

Artificial Intelligence in the Spotlight • MedicalExpo e-Magazine


Artificial intelligence (AI) was a key topic at both MEDICA and the RSNA conference this year. But what are its applications in healthcare in general and radiology in particular? And what are the barriers? Dr. Michael Forsting, director of the Institute of Diagnostic and Interventional Radiology and...

Accurate AI: Machine learning models identify findings in radiology reports


Machine learning models can identify key information in radiology reports with significant accuracy, according to a new study published in Radiology.

5 key takeaways from a new report on AI, machine learning in radiology


Research firm Reaction Data has published a new report, "Machine Learning in Medical Imaging," that breaks down what radiologists and other imaging professionals think about AI, machine learning and the future of radiology.

Natural Language–based Machine Learning Models for the Annotation of Clinical Radiology Reports


In this study, 96 303 head computed tomography (CT) reports were obtained. The linguistic complexity of these reports was compared with that of alternative corpora. Head CT reports were preprocessed, and machine-analyzable features were constructed by using bag-of-words (BOW), word embedding, and Latent Dirichlet allocation–based approaches. Ultimately, 1004 head CT reports were manually labeled for findings of interest by physicians, and a subset of these were deemed critical findings. Lasso logistic regression was used to train models for physician-assigned labels on 602 of 1004 head CT reports (60%) using the constructed features, and the performance of these models was validated on a held-out 402 of 1004 reports (40%).