Abstract

Abstract
Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks. This article is categorized under: Technologies > Machine Learning Technologies > Classification Application Areas > Health Care

Abstract
The optic disc (OD) and the fovea are relevant landmarks in fundus images. Their localization and segmentation can facilitate the detection of some retinal lesions and the assessment of their importance to the severity and progression of several eye disorders. Distinct methodologies have been developed for detecting these structures, mainly based on color and vascular information. The methodology herein described combines the entropy of the vessel directions with the image intensities for finding the OD center and uses a sliding band filter for segmenting the OD. The fovea center corresponds to the darkest point inside a region defined from the OD position and radius. Both the Messidor and the IDRiD datasets are used for evaluating the performance of the developed methods. In the first one, a success rate of 99.56% and 100.00% are achieved for OD and fovea localization. Regarding the OD segmentation, the mean Jaccard index and Dice’s coefficient obtained are 0.87 and 0.94, respectively. The proposed methods are also amongst the top-3 performing solutions submitted to the IDRiD online challenge.

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.