Abstract
Currently, lung cancer is the most lethal in the world. In order to make screening and follow-up a little more systematic, guidelines have been proposed. Therefore, this study aimed to create a diagnostic support approach by providing a patient label based on the LUNG-RADSTM guidelines. The only input required by the system is the nodule centroid to take the region of interest for the input of the classification system. With this in mind, two deep learning networks were evaluated: a Wide Residual Network and a DenseNet. Taking into account the annotation uncertainty we proposed to use sample weights that are introduced in the loss function, allowing nodules with a high agreement in the annotation process to take a greater impact on the training error than its counterpart. The best result was achieved with the Wide Residual Network with sample weights achieving a nodule-wise LUNG-RADSTM labelling accuracy of 0.735 +/- 0.003.

Abstract

Abstract
We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step to a challenging multi-task segmentation problem and show large increases in performance on all tasks, with Dice score increases over a no-enhancement baseline by as much as 0.491. We provide evidence that our enhancement preprocessing is useful for unbalanced and difficult data. We show that the enhancements can perform class balancing by composing them together.

Abstract

Abstract
Diabetic macular edema is a leading cause of visual loss for patients with diabetes. While diagnosis can only be performed by optical coherence tomography, diabetic macular edema risk assessment is often performed in eye fundus images in screening scenarios through the detection of hard exudates. Such screening scenarios are often associated with large amounts of data, high costs and high burden on specialists, motivating then the development of methodologies for automatic diabetic macular edema risk prediction. Nevertheless, significant dataset domain bias, due to different acquisition equipment, protocols and/or different populations can have significantly detrimental impact on the performance of automatic methods when transitioning to a new dataset, center or scenario. As such, in this study, a method based on residual neural networks is proposed for the classification of diabetic macular edema risk. This method is then validated across multiple public datasets, simulating the deployment in a multi-center setting and thereby studying the method’s generalization capability and existing dataset domain bias. Furthermore, the method is tested on a private dataset which more closely represents a realistic screening scenario. An average area under the curve across all public datasets of 0.891 ± 0.013 was obtained with a ResNet50 architecture trained on a limited amount of images from a single public dataset (IDRiD). It is also shown that screening scenarios are significantly more challenging and that training across multiple datasets leads to an improvement of performance (area under the curve of 0.911 ± 0.009).

Abstract
Diabetic retinopathy (DR) is a diabetes complication, which in extreme situations may lead to blindness. Since the first stages are often asymptomatic, regular eye examinations are required for an early diagnosis. As microaneurysms (MAs) are one of the first signs of DR, several automated methods have been proposed for their detection in order to reduce the ophthalmologists' workload. Although local convergence filters (LCFs) have already been applied for feature extraction, their potential as MA enhancement operators was not explored yet. In this work, we propose a sliding band filter for MA enhancement aiming at obtaining a set of initial MA candidates. Then, a combination of the filter responses with color, contrast and shape information is used by an ensemble of classifiers for final candidate classification. Finally, for each eye fundus image, a score is computed from the confidence values assigned to the MAs detected in the image. The performance of the proposed methodology was evaluated in four datasets. At the lesion level, sensitivities of 64% and 81% were achieved for an average of 8 false positives per image (FPIs) in e-ophtha MA and SCREEN-DR, respectively. In the last dataset, an AUC of 0.83 was also obtained for DR detection.