Machine learning methods are often evaluated on benchmark datasets, in computer vision, medical imaging, NLP and other fields. In such evaluation, researchers often describe the data as being: representative, for example based on the distribution of ages of the patients mirroring the world population, similar, for example because both dataset contain pictures of animals diverse, for example …
Supervisors: Veronika Cheplygina
Semester: Spring 2025
Tags: machine learning, medical imaging, data analysis, meta-research

Spectral learning priority is a useful tool in analyzing a model’s focus during training, it describes how a model may understand a given image from the spectrum perspective. For example, to distinguish cats and tortoises, learning to recognize their shapes would be enough, such embedding will result in higher learning priority at low frequencies representing shapes; while learning to …
Supervisors: Yucheng Lu, Veronika Cheplygina
Semester: Fall 2024
Tags: Spectral analysis, Image classification, Medical imaging

It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors: Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging

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: Spring 2025
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

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