Tagged with: medical imaging


PROPOSAL

Machine learning models, especially larger models that are used in for example image or text datasets, can be expensive to train. During development models are usually trained multiple times for example to optimize hyperparameters, which can result in a large carbon footprint. This project specifically focuses specifically on medical data. There are some recent efforts, for example …
Supervisors: Veronika Cheplygina
Semester: Fall 2022
Tags: machine learning, medical imaging, data analysis, resource consumption

PROPOSAL

There have been several situations where machine learning classifiers, trained to diagnose a particular disease (for example, lung cancer from chest x-rays), overfit on hidden features within the data. Examples include gridlines, surgical markers or evidence of treatment or text present in the images (see references for examples). This causes the classifier to fail on other type of images. …
Supervisors: Veronika Cheplygina
Semester: Fall 2022
Tags: machine learning, data science, medical imaging

PROPOSAL

Machine learning is used extensively in different applications, including medical imaging and natural language processing. As different types of data are involved, it is reasonable to assume that different methods are needed for each application. However, there are also opportunities in translating a method successful in one application, to the other application where it is not widely used. The …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, natural language processing, medical imaging, literature review

PROPOSAL

Machine learning challenges hosted on platforms such as Kaggle (general) or grand-challenge.org (medical imaging) have attracted a lot of attention, both from academia and industry researchers. Challenge designs vary widely [1], including what type of data is available, how the algorithms are evaluated, and the rewards for the winners. In medical imaging, there is some evidence that challenges …
Supervisors: Veronika Cheplygina
Semester: Fall 2022
Tags: machine learning, data science, medical imaging

PROPOSAL

Machine learning algorithms for skin lesion classification typically learn from images which have been labeled as malignant (for example, melanoma) or not. Such tasks can still suffer from overfitting due to limited dataset size. In other computer vision tasks, crowdsourcing labels has been effective, but the average person typically does not have the background to classify skin lesions. However, …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, medical imaging, crowdsourcing, similarity