Rank based annotation system for supervised learning in medical imaging
| dc.contributor.author | Tärnåsen, Hanna | |
| dc.contributor.author | Bergström, Herman | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för elektroteknik | sv |
| dc.contributor.examiner | Häggström, Ida | |
| dc.date.accessioned | 2023-06-15T18:29:00Z | |
| dc.date.available | 2023-06-15T18:29:00Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | Abstract Supervised learning has become a common approach for extracting information from images. To effectively train a model, a large amount of labeled data is required. While some image annotation tasks are objective and well-defined, others require the annotators to make a subjective assessment. The difficulty and subjective nature of these annotation tasks cause the standard rating-based annotation techniques to suffer from inconsistencies between annotators, implying that two different annotators could assign highly differing labels based on their personal biases. This thesis’ overarching goal is to provide an alternate rank-based system for annotating subjective data that could be applied to supervised learning, with the hope of increasing the quality of labels. The target application for this project is the annotation of the degree of bronchial wall thickening seen in CT scans of the lungs in patients with chronic obstructive pulmonary disease (COPD). Four potential implementations are compared, and consistency, as well as resource demands, are evaluated in several parts. These include imitating the annotation process with simulation, user evaluation with arbitrary subjective assessments, and lastly evaluating bronchial wall thickenings with radiologists. After evaluation, it is observed that the implementation showing the most potential is one based on the TrueSkill algorithm, which employs Bayesian inference and assumes that underlying scores are not definite but instead follows a normal distribution. The findings presented in this thesis indicate a clear increase in inter-annotator agreement for this rank-based system and the study demonstrates that the indirect approach of evaluating images creates more reliable labels than the direct ratingbased method | |
| dc.identifier.coursecode | EENX30 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/306251 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.title | Rank based annotation system for supervised learning in medical imaging | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Data science and AI (MPDSC), MSc |