Ivan Štajduhar gave a lecture titled “Hands-on Guide to Machine Learning Application” at the 2018 Summer School on Image Processing (SSIP) in Graz, Austria, on July 18, 2018. https://ssip2018.medunigraz.at/
Machine Learning Laboratory
Mirroring Quasi-Symmetric Organ Observations for Reducing Problem Complexity
Following an obvious growth of available collections of medical images in recent years, both in number and in size, machine learning has nowadays become an important tool for solving various image-analysis-related problems, such as organ segmentation or injury/pathology detection. The potential of learning algorithms to produce models having good generalisation properties is highly dependent on […]
Semi-automated detection of anterior cruciate ligament injury from MRI
Background and objectives: A radiologist’s work in detecting various injuries or pathologies from radiological scans can be tiresome, time consuming and prone to errors. The field of computer-aided diagnosis aims to reduce these factors by introducing a level of automation in the process. In this paper, we deal with the problem of detecting the presence […]
Uncensoring censored data for machine learning: A likelihood-based approach
Various machine learning techniques have been applied to different problems in survival analysis in the last decade. They were usually adapted to learning from censored survival data by using the information on observation time. This includes learning from parts of the data or interventions to the learning algorithms. Efficient models were established in various fields […]
Learning Bayesian networks from survival data using weighting censored instances
Different survival data pre-processing procedures and adaptations of existing machine-learning techniques have been successfully applied to numerous fields in clinical medicine. Zupan et al. (2000) proposed handling censored survival data by assigning distributions of outcomes to shortly observed censored instances. In this paper, we applied their learning technique to two well-known procedures for learning Bayesian […]
Impact of censoring on learning Bayesian networks in survival modelling
ObjectiveBayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their […]
