Although for many diseases there is a progressive diagnosis scale, automatic analysis of grade-based medical images is quite often addressed as a binary classification problem, …
This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional …
Objective To create a mandibular shape prediction model using machine learning techniques and geometric morphometrics. Materials and methods Six hundred twenty-nine …
TC Ni�o-Sandoval, RA Jaque, FA González | Clinical Oral… |
We demonstrate the implementation of a novel machine learning framework for classification and probability density estimation using quantum circuits. The framework maps a training …
This article presents a novel classical-quantum anomaly detection model based on density estimation and the expected values of density matrices. The core subroutine of the proposed …
In this article, the authors present a case study of an intervention in an introductory programming course aimed at improving the learning experience of students through the use of three …
This paper reports a novel method for supervised machine learning based on the mathematical formalism that supports quantum mechanics. The method uses projective quantum …
This paper shows that least-square estimation (mean calculation) in a reproducing kernel Hilbert space (RKHS) F corresponds to different M-estimators in the original space depending …
A density matrix describes the statistical state of a quantum system. It is a powerful formalism to represent both the quantum and classical uncertainty of quantum systems and to express …
Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the …
M delaPava, H R�os, FJ Rodr�guez, OJ Perdomo | arXiv preprint arXiv… | ( PDF )
We present a map from the travelling salesman problem (TSP), a prototypical NP-complete combinatorial optimisation task, to the ground state associated with a system of many-qudits. …
This paper presents DeepBoSE, a novel deep learning model for depression detection in social media. The model is formulated such that it internally computes a differentiable Bag-of-…
Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most …
This paper considers the problem of leveraging multiple sources of information or data modalities (eg, images and text) in neural networks. We define a novel model called gated …
This article describes the use of an automatic tool that supports both formative and summative evaluation, in an introductory computer programming course. The aim was to test the tool …
Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models …
Image acquisition and automatic quality analysis are fundamental stages and tasks to support an accurate ocular diagnosis. In particular, when eye fundus image quality is not …
This paper presents an information fusion method for the automatic classification and retrieval of prostate histopathology whole-slide images (WSIs). The approach employs a weakly-…
The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind …
Recently, the use of neural quantum states for describing the ground state of many- and few-body problems has been gaining popularity because of their high expressivity and ability to …
Passage retrieval is an important stage of question answering systems. Closed domain passage retrieval, eg biomedical passage retrieval presents additional challenges such as …
Passage retrieval is the task of identifying text snippets that are valid answers for a natural language posed question. One way to address this problem is to look at it as a metric learning …