Machine learning has been recently proposed for various medical applications. Especially the deep neural network based approach has been found to achieve state-of-the-art performance in various classification tasks. However, many of these studies use simplified classification systems, for example, the referable/non-referable system in the case of diabetic retinopathy classification. Moreover, the studies that have used clinical classification systems have not considered the uncertainty of the classifiers, which is of paramount interest in the medical field. In addition, extensive analysis of automatic segmentation algorithms that includes comparison to the interobserver variability of multiple radiologists' segmentations has not yet been performed for some challenging tasks, such as the automatic segmentation of the mandibular canals. The machine learning algorithms should also be able to be trained on local hospital data, which can pose issues relating to the amount of available training data. This thesis considers machine learning for various tasks in healthcare using Finnish hospital data. Deep convolutional neural networks (CNNs) are utilized for diabetic retinopathy and macular edema classification based on clinical severity scales. In addition, approximate Bayesian deep learning approaches are systematically studied for uncertainty-aware diabetic retinopathy classification of clinical data. A connection is derived between the referral of uncertain classifications and reject option classification, and it is used to develop a novel uncertainty measure. A CNN approach will also be introduced for the segmentation of the mandibular canal in cone beam computed tomography volumes. The approach is then compared to the interobserver variability of multiple radiologists' segmentations of the canal. Lastly, this thesis will examine multiple machine learning approaches for very low birth weight neonate mortality and morbidity prediction. The results suggest that even a relatively small set of Finnish hospital data can be utilized to train deep learning classifiers for diabetic retinopathy and macular edema classification with clinical classification systems. It also turns out that approximate Bayesian neural networks and the derived novel uncertainty measure can be used to accurately estimate the uncertainty in clinical diabetic retinopathy classification. The deep learning approach is shown to set a new state-of-the-art for the mandibular canal segmentation task and it is also found to localize the canals with lower variability than the interobserver variability of four radiologists. A random forest classifier turned out to outperform other methods in neonatal mortality and morbidity prediction.
|Translated title of the contribution||Koneoppiminen terveydenhuollossa|
|Publication status||Published - 2022|
|MoE publication type||G5 Doctoral dissertation (article)|
- machine learning
- deep learning
- approximate Bayesian deep learning