There is pressure on hospitals to implement AI systems which promise to improve diagnoses and save time for the doctors. One use-case could be related to the automation of protocoling based on a physician referral. Currently, this requires a referral letter from a physician who has examined a patient and evaluates that there is a need for additional imaging studies. In this case, the physician …
Supervisors: Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis

In medical imaging, multi-task learning can be used to train a model that jointly predicts both a diagnosis, and other patient characteristics, such as demographic variables. Among others, this strategy has frequently been used for diagnosis of Alzheimer’s from brain MR scans, with age as an additional variable, see Zhang et al as an example. The idea is that both the disease, and age, …
Supervisors: Veronika Cheplygina
Semester: Fall 2022
Tags: machine learning, medical imaging, data analysis, fairness

Concept Bottleneck Models [1] are designed to leverage high-level concepts. They revisit the classic idea of first predicting concepts that are providing at training time, and then using these concepts to predict the label. By construction, it is possible to intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. …
Supervisors: Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: machine learning, data science, medical imaging

A medical Visual Question Answering (VQA) system can provide meaningful references for both doctors and patients during the treatment process. Different from normal images, a learning setting with medical images is more challenging due limited amounts of data, class-imbalance and the presence of label noise for diagnosis tasks. Moreover, little attention is paid to how the images and meta-data is …
Supervisors: Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: medical imaging, deep learning, machine learning, transfer learning, meta-learning

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 by Selvan et …
Supervisors: Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis, resource consumption

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, Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: machine learning, data science, medical imaging

Deep neural networks have been revolutionary in computer vision and publicly available image datasets played an important role in this success. Due to their size, neural networks require vast amounts of data for training. Yet when it comes to medical settings dataset sizes are very limited due to the cost of data annotation, privacy concerns, differences in imaging techniques, and others. In such …
Supervisors: Dovile Juodelyte
Semester: Fall 2023
Tags: transfer learning, deep learning, medical imaging

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

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