Event
Public PhD defence of Panagiotis Gonidakis on July 1
 
 

On July 1st 2024 at 16:00, Panagiotis Gonidakis will defend their PhD entitled “DATA- AND LABEL-EFFICIENT DEEP LEARNING FOR MEDICAL IMAGE ANALYSIS APPLICATION TO LUNG NODULE DETECTION ON THORACIC CT”.

Everybody is invited to attend the presentation in room I.0.01, or digitally via this link.

Abstract 

Convolutional neural networks (CNNs) have been widely used to detect and classify various objects and structures in computer vision and medical imaging. Access to large sets of annotated data is commonly a prerequisite for achieving good performance. In medical imaging, acquiring adequate amounts of labelled data can often be time consuming and costly. Therefore, reducing the need for data and in particular associated annotations, is of high importance for medical imaging applications. In this work we investigated whether we can lower the need of annotated data for a supervised learning classification problem.  

We chose to tackle the problem of lung nodule detection in thoracic computed tomography (CT) imaging, as this widely investigated application allowed us to benefit from publicly available data and benchmark our methods. We designed a 3D CNN architecture to perform patch-wise classification of candidate nodules for false positive reduction. Its training, testing and fine-tuning procedure is optimized, we evaluated its performance, and we compared it with other state-of-the-art approaches in the field.  

Next, we explored how data augmentation can contribute towards more accurate and less data-demanding models. We investigated the relative benefit of increasing the amount of original data, with respect to computationally augmenting the amount of training samples. Our result indicated that in general, better performance is achieved when increasing the amount of unique data samples, or augmenting the data more extensively, as expected. Surprisingly however, we observed that after reaching a certain amount of training samples, data augmentation led to significantly better performance compared to adding unique samples. Amongst investigated augmentation methods, rotations were found to be most beneficial for improving model performance. 

Following, we investigated the benefit of combining deep learning with handcrafted features. We explored three fusion strategies with increasing complexity and assessed their performance for varying amounts of training data. Our findings indicated that combining handcrafted features with a 3D CNN approach significantly improved lung nodule detection performance in comparison to an independently trained CNN model, regardless of the fusion strategy. Comparatively larger increases in performance were obtained when less training data was available. The fusion strategy in which features are combined with a CNN using a single end-to-end training scheme performed best overall, allowing to reduce training data by 33% to 43%, while maintaining performance. Among the investigated handcrafted features, those that describe the relative position of the candidate with respect to the lung wall and mediastinum, were found to be of most benefit. 

Finally, we considered the case in which abundant data is available, but annotations are scarce, and investigated several methods to improve label-efficiency and their combined effect. We proposed a framework that utilizes both annotated and unannotated data, can be pretrained via self-supervision, and allows to combine handcrafted features with learned representations. Interestingly, the improvements in performance derived from the proposed learning schemes were found to accumulate, leading to increased label-efficiency when these strategies are combined. We observed a potential to decrease the amount of annotated data up to 68% when compared to traditional supervised training, while maintaining performance.  

Our findings indicate that the investigated methods allow considerable reduction of data and/or annotations while maintaining model performance for lung nodule detection from CT imaging. Future work should investigate whether these results generalize to other domains, such that more applications that face challenges due to a shortage of annotated data may benefit from the potential of deep learning.

 
 
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