Daniel Flores-Araiza

Tecnológico de Monterrey

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Co-Authors

Gilberto Ochoa‐Ruiz

Tecnológico de Monterrey

Francisco Lopez-Tiro

Tecnológico de Monterrey

Christian Daul

Université de Lorraine

Jacques Hubert

Centre Hospitalier Régional et Universitaire de Nancy

Andres Méndez-Vázquez

Center for Research and Advanced Studies of the National Polytechnic Institute

Miguel González-Mendoza

Tecnológico de Monterrey

Ivan Reyes-Amezcua

Center for Research and Advanced Studies of the National Polytechnic Institute

Jorge Gonzalez-Zapata

Center for Research and Advanced Studies of the National Polytechnic Institute

Mauricio Méndez-Ruiz

Tecnológico de Monterrey

Jonathan El-Beze

Centre Hospitalier Régional et Universitaire de Nancy

Cooperative Institutions

Weatherford College

Tecnológico de Monterrey

Adrian College

Directorate of Medicinal and Aromatic Plants Research

ETH Zurich

Seoul National University

Google (United States)

Istituto Tecnico Industriale Alessandro Volta

Chinese Academy of Sciences

Université de Lorraine

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LatinX in AI at Computer Vision and Pattern Recognition Conference 2022

Jorge Gonzalez-Zapata Ivan Reyes-Amezcua Daniel Flores-Araiza Mauricio Méndez-Ruiz Gilberto Ochoa‐Ruiz

Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning.However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused by a distributional shift in the data.These represent a significant drawback when attempting to learn the underlying data manifold.Therefore, there is a pressing need to develop better ways of obtaining generalization and representation of the underlying manifold.In this paper, we propose a novel approach to DML that we call Guided Deep Metric Learning, a novel architecture oriented to learning more compact clusters, improving generalization under distributional shifts in DML.This novel architecture consists of two independent models: A multi-branch master model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced hypothesis space based on prior knowledge from labeled data, which guides or regularizes the decision boundary of a student model during training under an offline knowledge distillation scheme.Experiments have shown that the proposed method is capable of a better manifold generalization and representation to up to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave et al. to perform a more fair and realistic comparison, which is currently absent in the literature.

10.52591/lxai202206241

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On the in vivo recognition of kidney stones using machine learning

arXiv (Cornell University) (2022)

Gilberto Ochoa‐Ruiz V. Estrade Francisco J. Martínez‐López Daniel Flores-Araiza Jonathan El Beze

Determining the type of kidney stones allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies. This contribution details the database construction and the design of the tested kidney stones classifiers. Even if the best results were obtained by the Inception v3 architecture (weighted precision, recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted precision, recall and F1-score values of 0.96). This paper is the first one that explores the most discriminant features to be extracted from images acquired during ureteroscopies.

10.48550/arxiv.2201.08865

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Citations (5)

FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

Lecture notes in computer science (2023)

Pablo Cesar Quihui-Rubio Daniel Flores-Araiza Miguel González-Mendoza Christian Mata Gilberto Ochoa‐Ruiz

Pyramid (geometry)

Net (polyhedron)

Feature (linguistics)

10.1007/978-3-031-47640-2_14

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MACFE: A Meta-learning and Causality Based Feature Engineering Framework

arXiv (Cornell University) (2022)

Ivan Reyes-Amezcua Daniel Flores-Araiza Gilberto Ochoa‐Ruiz Andres Méndez-Vázquez Eduardo Rodríguez-Tello

Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial domain-knowledge which involves a time-consuming process. Thereby, automating such process has become an active area of research and of interest in industrial applications. In this paper, a novel method, called Meta-learning and Causality Based Feature Engineering (MACFE), is proposed; our method is based on the use of meta-learning, feature distribution encoding, and causality feature selection. In MACFE, meta-learning is used to find the best transformations, then the search is accelerated by pre-selecting "original" features given their causal relevance. Experimental evaluations on popular classification datasets show that MACFE can improve the prediction performance across eight classifiers, outperforms the current state-of-the-art methods in average by at least 6.54%, and obtains an improvement of 2.71% over the best previous works.

Feature Engineering

Feature (linguistics)

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Causality

10.48550/arxiv.2207.04010

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2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2022)

Abbott Lynn Mohamed Abdelsamad Waleed H. Abdulla Aberman Kfir Vitaly Ablavsky

10.1109/cvprw56347.2022.00572

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Enforcing Class Separability in Metric Learning Via Two Novel Distance-Based Loss Functions for Few-Shot Image Classification

Gilberto Ochoa‐Ruiz Mauricio Méndez-Ruiz Jorge Gonzalez-Zapata Ivan Reyes-Amezcua Daniel Flores-Araiza

Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches leverage the meta-learning approach, which is encompassed by episodic tasks that make use a support (training) and query set (test) with the objective of learning a similarity comparison metric between those sets. Due to the lack of data, the learning process of the embedding network becomes an important part of the few-shot task. Previous works have addressed this problem using metric learning approaches, but the properties of the underlying latent space and the separability of the difference classes on it was not entirely enforced. In this work, we propose two different loss functions which consider the importance of the embedding vectors by looking at the intra-class and inter-class distance between the few data. The first loss function is the Proto-Triplet Loss, which is based on the original triplet loss with the modifications needed to better work on few-shot scenarios. The second loss function, which we dub ICNN loss is based on an inter and intra class nearest neighbors score, which help us to assess the quality of embeddings obtained from the trained network. Our results, obtained from a extensive experimental setup show a significant improvement in accuracy in the miniImagenNet benchmark compared to other metric-based few-shot learning methods by a margin of 2%, demonstrating the capability of these loss functions to allow the network to generalize better to previously unseen classes. In our experiments, we demonstrate competitive generalization capabilities to other domains, such as the Caltech CUB, Dogs and Cars datasets compared with the state of the art.

One shot

10.2139/ssrn.4488078

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A metric learning approach for endoscopic kidney stone identification

Expert Systems with Applications (2024)

Jorge Gonzalez-Zapata Francisco Lopez-Tiro Elias Villalvazo-Avila Daniel Flores-Araiza Jacques Hubert

Identification

10.1016/j.eswa.2024.124711

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Citations (1)

Identification and PID control for a quadrocopter

Irma Pérez Daniel Flores-Araiza Jesús Anselmo Fortoul-Diaz R. Maximo Hugo G. González-Hernández

Identification and control of unmanned aerial vehicles (UAV) has been a topic of interest in recent years due to several applications they are used for, some examples could be: surveillance or cooperative robotics. This paper shows the parameter identification and control of the AR Drone®, a quadrocopter from Parrot®; the parameter identification was achieved using experimental data from the physical system. For the control part a PID control was used to regulate all the variables of the Drone.

Drone

Identification

10.1109/conielecomp.2014.6808571

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Citations (9)

LatinX in AI at Computer Vision and Pattern Recognition Conference 2022

Jorge Gonzalez-Zapata Ivan Reyes-Amezcua Daniel Flores-Araiza Mauricio Méndez-Ruiz Gilberto Ochoa‐Ruiz

10.52591/lxai202206240

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Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2023)

Daniel Flores-Araiza Francisco Lopez-Tiro Jonathan El-Beze Jacques Hubert Miguel González-Mendoza

Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses. Currently, the associated ex-vivo diagnosis (known as Morpho-constitutional Analysis, MCA) is time-consuming, expensive and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Deep Learning (DL) based methods outperform non-DL methods in terms of accuracy but lack explainability. Despite this trade-off, when it comes to making high-stakes decisions, it's important to prioritize understandable Computer-Aided Diagnosis (CADx) that suggests a course of action based on reasonable evidence, rather than a model prescribing a course of action. In this proposal, we learn Prototypical Parts (PPs) per kidney stone subtype, which are used by the DL model to generate an output classification. Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable. In addition, we modify global visual characteristics to describe their relevance to the PPs and the sensitivity of our model's performance. With this, we provide explanations with additional information at the sample, class and model levels in contrast to previous works. Although our implementation's average accuracy is lower than state-of-the-art (SOTA) non-interpretable DL models by 1.5%, our models perform 2.8% better on perturbed images with a lower standard deviation, without adversarial training. Thus, Learning PPs has the potential to create more robust DL models. Code at: https://github.com/DanielF29/Prototipical-Parts

Relevance

10.1109/cvprw59228.2023.00035

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Citations (4)