Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT

The classification of benign versus malignant lung nodules using chest CT plays a pivotal role in the early detection of lung cancer and this early detection has the best chance of cure. Although deep learning is now the most successful solution for image classification problems, it requires a myriad number of training data, which are not usually readily available for most routine medical imaging applications. In this paper, we propose the transferable multi-model ensemble (TMME) algorithm to separate malignant from benign lung nodules using limited chest CT data. This algorithm transfers the image representation abilities of three ResNet-50 models, which were pre-trained on the ImageNet database, to characterize the overall appearance, heterogeneity of voxel values and heterogeneity of shape of lung nodules, respectively, and jointly utilizes them to classify lung nodules with an adaptive weighting scheme learned during the error back propagation. Experimental results on the benchmark LIDC-IDRI dataset show that our proposed TMME algorithm achieves a lung nodule classification accuracy of 93.40%, which is markedly higher than the accuracy of seven state-of-the-art approaches

What is the ground truth? Reliability of multi-annotator data for audio tagging

Crowdsourcing has become a common approach for annotating large amounts of data. It has the advantage of harnessing a large workforce to produce large amounts of data in a short time, but comes with the disadvantage of employing non-expert annotators with different backgrounds. This raises the problem of data reliability, in addition to the general question of how to combine the opinions of multiple annotators in order to estimate the ground truth. This paper presents a study of the annotations and annotators' reliability for audio tagging. We adapt the use of Krippendorf's alpha and multi-annotator competence estimation (MACE) for a multi-labeled data scenario, and present how MACE can be used to estimate a candidate ground truth based on annotations from non-expert users with different levels of expertise and competence.Comment: submitted to EUSIPCO 202

Segmentation of roots in soil with U-Net

Demonstration of the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method

Improving filtering methods based on the Fast Fourier Transform to delineate objective relief domains: An application to Mare Ingenii lunar area

A recent study has proven that high-pass filtering (HPF) based on the Fast Fourier Transform (FFT) is a rapid and efficient computational method for the semi-automated detection of geomorphic features from high-resolution digital elevation models (DEM). Although this new approach shows great potential for cartographic purposes using remote sensing data, some methodological improvements are still required in the following areas: (i) to develop a robust criteria for filter radius selection; (ii) to test the relationship between filter vectors and landscape form, and explore how DEM artefacts (vegetation, anthropic structures, etc.) can interfere with landform detection; and (iii) to explore filter response regarding generalisation and blurring effects when working with landscapes composed of landforms of different scales that are superimposed on one another. These topics are addressed here through two experiments (Experiment_1 and Experiment_2) with synthetic digital relief models inspired in the lunar landscape. Finally, the improved methodology was applied on the Mare Ingenii lunar relief (Experiment_3) using the Lunar Orbiter Laser Altimeter DEM and the results were tested against ground truths (GTs) developed using the extensive database available at Astropedia website and an ad hoc crater map. The analysis of existing frequencies in a 2D DEM signal through the true magnitude-true frequency plot provides an objective method for filter radius selection, and the use of a Butterworth transference function enables a more versatile filtering. Experiment_1 demonstrates a close correspondence between vectors obtained by filtering called Filtered Geomorphic References (FGRs) and the synthetic landform selected. The accuracy indicators from Experiment_1 and 2 show the good results obtained in the correspondence between FGRs and crater depressions, either from flat-bottomed to bowl shapes. Experiments 2 and 3 confirm that in landscapes generated by superimposed geomorphic features of different sizes, the smaller the crater, the better the filters detect its boundaries. Moreover, the spatial repeatability of FGRs can be used as a cartographic criterion in the identification of crater shape depressions or hills. Besides, the criterion is useful to assess true reality mapped in the GT employed. Finally, the objective geomorphic units obtained by combining the FGRs demonstrate their usefulness for the objective characterisation of the moonscape. Using the synthetic landscapes, the FGRs identify those relief domains composed of depressions and hills.This work was carried out as part of the Projects: 29.P114.64004 (UC); 29.P203.64004 (UC); RECORNISA (FLTQ-UC)

An Empirical Study Into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation

Although agreement between annotators has been studied in the past from a statistical viewpoint, little work has attempted to quantify the extent to which this phenomenon affects the evaluation of computer vision (CV) object detection algorithms. Many researchers utilise ground truth (GT) in experiments and more often than not this GT is derived from one annotator's opinion. How does the difference in opinion affect an algorithm's evaluation? Four examples of typical CV problems are chosen, and a methodology is applied to each to quantify the inter-annotator variance and to offer insight into the mechanisms behind agreement and the use of GT. It is found that when detecting linear objects annotator agreement is very low. The agreement in object position, linear or otherwise, can be partially explained through basic image properties. Automatic object detectors are compared to annotator agreement and it is found that a clear relationship exists. Several methods for calculating GTs from a number of annotations are applied and the resulting differences in the performance of the object detectors are quantified. It is found that the rank of a detector is highly dependent upon the method used to form the GT. It is also found that although the STAPLE and LSML GT estimation methods appear to represent the mean of the performance measured using the individual annotations, when there are few annotations, or there is a large variance in them, these estimates tend to degrade. Furthermore, one of the most commonly adopted annotation combination methods--consensus voting--accentuates more obvious features, which results in an overestimation of the algorithm's performance. Finally, it is concluded that in some datasets it may not be possible to state with any confidence that one algorithm outperforms another when evaluating upon one GT and a method for calculating confidence bounds is discussed.Comment: 16 page

Improving the clinico-radiological association in neurological diseases

Despite the key role of magnetic resonance imaging (MRI) in the diagnosis and monitoring of multiple sclerosis (MS) and cerebral small vessel disease (SVD), the association between clinical and radiological disease manifestations is often only moderate, limiting the use of MRI-derived markers in the clinical routine or as endpoints in clinical trials. In the projects conducted as part of this thesis, we addressed this clinico-radiological gap using two different approaches. Lesion-symptom association: In two voxel-based lesion-symptom mapping studies, we aimed at strengthening lesion-symptom associations by identifying strategic lesion locations. Lesion mapping was performed in two large cohorts: a dataset of 2348 relapsing-remitting MS patients, and a population-based cohort of 1017 elderly subjects. T2-weighted lesion masks were anatomically aligned and a voxel-based statistical approach to relate lesion location to different clinical rating scales was implemented. In the MS lesion mapping, significant associations between white matter (WM) lesion location and several clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers. In the SVD lesion mapping, the same WM fibers and the caudate nucleus were identified to significantly relate to the subjects’ cerebrovascular risk profiles, while no other locations were found to be associated with cognitive impairment. Atrophy-symptom association: With the construction of an anatomical physical phantom, we aimed at addressing reliability and robustness of atrophy-symptom associations through the provision of a “ground truth” for atrophy quantification. The built phantom prototype is composed of agar gels doped with MRI and computed tomography (CT) contrast agents, which realistically mimic T1 relaxation times of WM and grey matter (GM) and showing distinguishable attenuation coefficients using CT. Moreover, due to the design of anatomically simulated molds, both WM and GM are characterized by shapes comparable to the human counterpart. In a proof-of-principle study, the designed phantom was used to validate automatic brain tissue quantification by two popular software tools, where “ground truth” volumes were derived from high-resolution CT scans. In general, results from the same software yielded reliable and robust results across scans, while results across software were highly variable reaching volume differences of up to 8%