Search results for "deep learning"
showing 10 items of 337 documents
Predicting the Success of Blastocyst Implantation from Morphokinetic Parameters Estimated through CNNs and Sum of Absolute Differences
2019
The process of In Vitro Fertilization deals nowadays with the challenge of selecting viable embryos with the highest probability of success in the implantation. In this topic, we present a computer-vision-based system to analyze the videos related to days of embryo development which automatically extracts morphokinetic features and estimates the success of implantation. A robust algorithm to detect the embryo in the culture image is proposed to avoid artifacts. Then, the ability of Convolutional Neural Networks (CNNs) for predicting the number of cells per frame is novelty combined with the Sum of Absolute Differences (SAD) signal in charge of capturing the amount of intensity changes durin…
Searching Silk Fabrics by Images Leveraging on Knowledge Graph and Domain Expert Rules
2021
The production of European silk textile is an endangered intangible cultural heritage. Digital tools can nowadays be developed to help preserving it, or even to make it more accessible for the public and the fashion industry. In this paper, we propose an image-based retrieval tool that leverages on a knowledge graph describing the silk textile production as well as rules formulated by experts of this domain. Out of several possible similarity scenarios, two have proven to work best and have been integrated into an exploratory search engine.
Enriching Didactic Similarity Measures of Concept Maps by a Deep Learning Based Approach
2021
Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students' assessment and many others. They are also successfully used in learning activities in which students have to represent domain knowledge according to teacher's assignment. In this context, the development of Learning Analytics approaches would benefit of methods that automatically compare concept maps. Detecting concept maps similarities is relevant to identify how the same concepts are used in different knowledge representations. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appea…
RDF* Graph Database as Interlingua for the TextWorld Challenge
2019
This paper briefly describes the top-scoring submission to the First TextWorld Problems: A Reinforcement and Language Learning Challenge. To alleviate the partial observability problem, characteristic to the TextWorld games, we split the Agent into two independent components: Observer and Actor, communicating only via the Interlingua of the RDF* graph database. The RDF* graph database serves as the “world model” memory incrementally updated by the Observer via FrameNet informed Natural Language Understanding techniques and is used by the Actor for the efficient exploration and planning of the game Action sequences. We find that the deep-learning approach works best for the Observer componen…
A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases
2020
Accurate three-dimensional (3D) cardiac segmentation from late gadolinium enhancement (LGE)-MRI plays a critical role in designing a structure of reference for diagnosing many cardiac pathologies such as ischemia, myocarditis and myocardial infarction. This segmentation is however still a non-trivial task, due to the motion artifacts during acquisition, and heterogeneous intensity distributions. In this study, we develop a fully 3D automated model based on deep neural networks (DNN) for LGE-MRI myocardial pathologies (scar and No-reflow tissues) segmentation in a new expert annotated dataset. Considering that damaged tissue constitutes a small area of the whole LGE-MRI, we concentrated on m…
Deep learning for knowledge tracing in learning analytics: An overview
2021
Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting, analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge Tracing (DKT). This was made possible by the digitalization process that has simplified t…
MoodlePeers: Factors Relevant in Learning Group Formation for Improved Learning Outcomes, Satisfaction and Commitment in E-Learning Scenarios Using G…
2016
High-scale and pure online learning scenarios (like MOOCs) as well as blended-learning scenarios offer great possibilities to optimize the composition of learning groups working together on the assigned (or selected) tasks. While the benefits and importance of peer learning for deep learning and improvement of e.g. problem-solving competency and social skills are indisputable, little evidences exist about the relevant factors for group formation and their combination to optimize the learning outcome for all participants (in all groups). Based on the GroupAL algorithm, MoodlePeers proposes an plugin solution for Moodle. Evaluated in a four-week online university mathematics preparation cours…
Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network
2020
Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT …
OLF : RGB-D Adaptive Late Fusion for Robust 6D Pose Estimation
2023
RGB-D 6D pose estimation has recently gained significant research attention due to the complementary information provided by depth data. However, in real-world scenarios, especially in industrial applications, the depth and color images are often more noisy. Existing methods typically employ fusion designs that equally average RGB and depth features, which may not be optimal. In this paper, we propose a novel fusion design that adaptively merges RGB-D cues. Our approach involves assigning two learnable weight α 1 and α 2 to adjust the RGB and depth contributions with respect to the network depth. This enables us to improve the robustness against low-quality depth input in a simple yet effec…
Identification of the most informative wavelengths for non-invasive melanoma diagnostics in spectral region from 450 to 950 nm
2020
In this study 300 skin lesion (including 32 skin melanomas) multispectral data cubes were analyzed. The multi-step and single step machine learning approaches were analyzed to find the wavebands that provide the most information that helps discriminate skin melanoma from other benign pigmented lesions. The multi-step machine learning approach assumed training several models but proved itself to be ineffective. The reason for that is a necessity to train a segmentation model on a very small dataset and utilization of standard machine learning classifier which have shown poor classification performance. The single-step approach is based on a deep learning neural network. We have conducted 260…