A Review on Machine Learning Methods in Diabetic Retinopathy Detection

Ocular disorders have a broad spectrum. Some of them, such as Diabetic Retinopathy, are more common in low-income or low-resource countries. Diabetic Retinopathy is a cause related to vision loss and ocular impairment in the world. By identifying the symptoms in the early stages, it is possible to prevent the progress of the disease and also reach blindness. Considering the prevalence of different branches of Artificial Intelligence in many fields, including medicine, and the significant progress achieved in the use of big data to investigate ocular impairments, the potential of Artificial Intelligence algorithms to process and analyze Fundus images was used to identify symptoms associated with Diabetic Retinopathy. Under the studies, the proposed models for transformers provide better interpretability for doctors and scientists. Artificial Intelligence algorithms are also helpful in anticipating future health issues after appraising premature cases of the ailment. Especially in ophthalmology, a trustworthy diagnosis of visual outcomes helps physicians in advising disease and clinical decision-making while reducing health management costs

Minimally Interactive Segmentation with Application to Human Placenta in Fetal MR Images

Placenta segmentation from fetal Magnetic Resonance (MR) images is important for fetal surgical planning. However, accurate segmentation results are difficult to achieve for automatic methods, due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta among pregnant women. Interactive methods have been widely used to get more accurate and robust results. A good interactive segmentation method should achieve high accuracy, minimize user interactions with low variability among users, and be computationally fast. Exploiting recent advances in machine learning, I explore a family of new interactive methods for placenta segmentation from fetal MR images. I investigate the combination of user interactions with learning from a single image or a large set of images. For learning from a single image, I propose novel Online Random Forests to efficiently leverage user interactions for the segmentation of 2D and 3D fetal MR images. I also investigate co-segmentation of multiple volumes of the same patient with 4D Graph Cuts. For learning from a large set of images, I first propose a deep learning-based framework that combines user interactions with Convolutional Neural Networks (CNN) based on geodesic distance transforms to achieve accurate segmentation and good interactivity. I then propose image-specific fine-tuning to make CNNs adaptive to different individual images and able to segment previously unseen objects. Experimental results show that the proposed algorithms outperform traditional interactive segmentation methods in terms of accuracy and interactivity. Therefore, they might be suitable for segmentation of the placenta in planning systems for fetal and maternal surgery, and for rapid characterization of the placenta by MR images. I also demonstrate that they can be applied to the segmentation of other organs from 2D and 3D images

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

The Monogenic Architecture of Retinal and Neurological Diseases

Monogenic diseases, or single-gene disorders, are clinical manifestations that can be traced to genetic variation in a single gene that alters the biologically intended (wildtype) function of its protein (or mRNA) product. Although the causal gene and its function are well-understood in many monogenic diseases, this knowledge alone often does not fully encapsulate the extensive clinical spectrum of phenotypes seen in patients. This is due in part to the numerous types of pathogenic variants that can arise in a single gene, all of which can have distinct effects on disease expression. Understanding the relationship between the vast number of possible genotypes and corresponding disease phenotypes defines a gene’s monogenic disease architecture—an important but poorly understood concept that can yield informative mechanistic and clinical insight. This doctoral dissertation integrates traditional sequencing approaches with in-depth characterization of patient phenotypes to elucidate the monogenic disease architecture of three etiologically distinct disorders: retinal degeneration caused by autosomal recessive variation in ABCA4 and neurodevelopmental disease entities caused by autosomal dominant variants in CERT1 and PUM1. Genetic modifiers are identified as a significant factor in the penetrance of the major disease-causing allele of ABCA4 and several other genetic inconsistencies are resolved to create a coherent genotype-phenotype model for the disease. Insight from this model is then applied to demonstrate the effect of allele differences in disease progression and evaluation of treatment efficacy in patients. A large cohort of affected individuals with CERT1 variation is assembled to (1) validate the causal role of CERT1 in disease, (2) delineate the precise mechanism of CERT protein dysfunction in sphingolipid metabolism and (3) demonstrate therapeutic efficacy of an inhibitor compound for a newly described syndrome. Finally, the mutational spectrum of PUM1 is expanded to previously unattributed variant classes with unexpected pathophysiological consequences to patients. Not only do the findings in this dissertation advance the prospects of delivering personalized, precision medicine to patients, the overall impact underscores the importance of this integrated approach in reconciling knowledge gaps between observations at the molecular and organismal level

Microscopy Conference 2021 (MC 2021) - Proceedings

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2021"

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words