Mexican International Conference on Artificial Intelligence最新文献

Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring 学习森林结构的神经辐射场，实现可扩展的精细监测

Mexican International Conference on Artificial Intelligence Pub Date : 2024-01-26 DOI: 10.1007/978-3-031-47640-2_23

Juan Castorena

引用次数: 0

Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital Fossa 基于边缘人工智能的静脉检测器，用于在眶前窝进行高效静脉穿刺

Mexican International Conference on Artificial Intelligence Pub Date : 2023-10-27 DOI: 10.1007/978-3-031-47640-2_24

Edwin Salcedo, Patricia Penaloza

引用次数: 0

Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS) 基于冲突搜索(CBS)和焦点搜索(FS)相结合的任意时刻MAPF求解器分析

Mexican International Conference on Artificial Intelligence Pub Date : 2022-09-20 DOI: 10.48550/arXiv.2209.09612

Ilya Ivanashev, A. Andreychuk, K. Yakovlev

{"title":"Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS)","authors":"Ilya Ivanashev, A. Andreychuk, K. Yakovlev","doi":"10.48550/arXiv.2209.09612","DOIUrl":"https://doi.org/10.48550/arXiv.2209.09612","url":null,"abstract":". Conﬂict-Based Search (CBS) is a widely used algorithm for solving multi-agent pathﬁnding (MAPF) problems optimally. The core idea of CBS is to run hierarchical search, when, on the high level the tree of solutions candidates is explored, and on the low-level an individual planning for a speciﬁc agent (subject to certain constraints) is carried out. To trade-oﬀ optimality for running time diﬀerent variants of bounded sub-optimal CBS were designed, which alter both high- and low-level search routines of CBS. Moreover, anytime variant of CBS does exist that applies Focal Search (FS) to the high-level of CBS – Anytime BCBS. However, no comprehensive analysis of how well this algorithm performs compared to the naive one, when we simply re-invoke CBS with the decreased sub-optimality bound, was present. This work aims at ﬁlling this gap. Moreover, we present and evaluate another anytime version of CBS that uses FS on both levels of CBS. Empirically, we show that its behavior is principally diﬀerent from the one demonstrated by Anytime BCBS. Finally, we compare both algorithms head-to-head and show that using Focal Search on both levels of CBS can be beneﬁcial in a wide range of setups.","PeriodicalId":166595,"journal":{"name":"Mexican International Conference on Artificial Intelligence","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127331895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures 基于u -net结构的MRI图像前列腺区自动分割模型的比较

Mexican International Conference on Artificial Intelligence Pub Date : 2022-07-19 DOI: 10.48550/arXiv.2207.09483

Pablo Cesar Quihui-Rubio, G. Ochoa-Ruiz, M. González-Mendoza, Gerardo Rodriguez-Hernandez, Christian Mata

{"title":"Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures","authors":"Pablo Cesar Quihui-Rubio, G. Ochoa-Ruiz, M. González-Mendoza, Gerardo Rodriguez-Hernandez, Christian Mata","doi":"10.48550/arXiv.2207.09483","DOIUrl":"https://doi.org/10.48550/arXiv.2207.09483","url":null,"abstract":". Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27-44%) or over-diagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features of the ROI from magnetic resonance images, as this process can greatly facilitate many diagnostic and therapeutic applications. However, the lack of clear prostate boundaries, the heterogeneity inherent to the prostate tissue, and the variety of prostate shapes makes this process very difﬁcult to automate.In this work, six deep learning models were trained and analyzed with a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and Universitat Politecnica de Catalunya. We carried out a comparison of multiple deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy loss function. The analysis was performed using three metrics commonly used for image segmentation: Dice score, Jaccard index, and mean squared error. The model that give us the best result segmenting all the zones was R2U-Net, which achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error, respectively.","PeriodicalId":166595,"journal":{"name":"Mexican International Conference on Artificial Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

MACFE: A Meta-learning and Causality Based Feature Engineering Framework MACFE:一个基于元学习和因果关系的特征工程框架

Mexican International Conference on Artificial Intelligence Pub Date : 2022-07-08 DOI: 10.48550/arXiv.2207.04010

Iván Reyes-Amezcua, Daniel Flores-Araiza, G. Ochoa-Ruiz, Andres Mendez-Vazquez, E. Rodriguez-Tello

引用次数: 0

A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models 用于评估基于机器学习的光度图像增强模型的新型混合内窥镜数据集

Mexican International Conference on Artificial Intelligence Pub Date : 2022-07-06 DOI: 10.48550/arXiv.2207.02396

Carlos Axel Garcia-Vega, Ricardo Espinosa, G. Ochoa-Ruiz, T. Bazin, L. Falcón-Morales, D. Lamarque, C. Daul

{"title":"A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models","authors":"Carlos Axel Garcia-Vega, Ricardo Espinosa, G. Ochoa-Ruiz, T. Bazin, L. Falcón-Morales, D. Lamarque, C. Daul","doi":"10.48550/arXiv.2207.02396","DOIUrl":"https://doi.org/10.48550/arXiv.2207.02396","url":null,"abstract":"Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs. However, images acquired by an endoscope are frequently affected by illumination artefacts due to the enlightenment source orientation. There exist two major issues when the endoscope's light source pose suddenly changes: overexposed and underexposed tissue areas are produced. These two scenarios can result in misdiagnosis due to the lack of information in the affected zones or hamper the performance of various computer vision methods (e.g., SLAM, structure from motion, optical flow) used during the non invasive examination. The aim of this work is two-fold: i) to introduce a new synthetically generated data-set generated by a generative adversarial techniques and ii) and to explore both shallow based and deep learning-based image-enhancement methods in overexposed and underexposed lighting conditions. Best quantitative results (i.e., metric based results), were obtained by the deep-learnnig-based LMSPEC method,besides a running time around 7.6 fps)","PeriodicalId":166595,"journal":{"name":"Mexican International Conference on Artificial Intelligence","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126300964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Real-Time Mexican Sign Language Interpretation Using CNN and HMM 使用CNN和HMM的实时墨西哥手语翻译

Mexican International Conference on Artificial Intelligence Pub Date : 2022-06-24 DOI: 10.1007/978-3-030-89817-5_4

Jairo Enrique Ramírez Sánchez, Arely Anguiano Rodríguez, M. González-Mendoza

引用次数: 3

Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation 深度学习方法中损失函数对视网膜血管精确分割的影响

Mexican International Conference on Artificial Intelligence Pub Date : 2022-06-01 DOI: 10.48550/arXiv.2206.00536

Daniela Herrera, G. Ochoa-Ruiz, M. González-Mendoza, Christian Mata

{"title":"Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation","authors":"Daniela Herrera, G. Ochoa-Ruiz, M. González-Mendoza, Christian Mata","doi":"10.48550/arXiv.2206.00536","DOIUrl":"https://doi.org/10.48550/arXiv.2206.00536","url":null,"abstract":"The retinal vessel network studied through fundus images contributes to the diagnosis of multiple diseases not only found in the eye. The segmentation of this system may help the specialized task of analyzing these images by assisting in the quantification of morphological characteristics. Due to its relevance, several Deep Learning-based architectures have been tested for tackling this problem automatically. However, the impact of loss function selection on the segmentation of the intricate retinal blood vessel system hasn't been systematically evaluated. In this work, we present the comparison of the loss functions Binary Cross Entropy, Dice, Tversky, and Combo loss using the deep learning architectures (i.e. U-Net, Attention U-Net, and Nested UNet) with the DRIVE dataset. Their performance is assessed using four metrics: the AUC, the mean squared error, the dice score, and the Hausdorff distance. The models were trained with the same number of parameters and epochs. Using dice score and AUC, the best combination was SA-UNet with Combo loss, which had an average of 0.9442 and 0.809 respectively. The best average of Hausdorff distance and mean square error were obtained using the Nested U-Net with the Dice loss function, which had an average of 6.32 and 0.0241 respectively. The results showed that there is a significant difference in the selection of loss function","PeriodicalId":166595,"journal":{"name":"Mexican International Conference on Artificial Intelligence","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132078961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification 基于域自适应的FSL方法的泛化能力——以肾结石内镜图像分类为例

Mexican International Conference on Artificial Intelligence Pub Date : 2022-05-02 DOI: 10.48550/arXiv.2205.00895

M. Mendez-Ruiz, F. Lopez-Tiro, Jonathan El Beze, V. Estrade, G. Ochoa-Ruiz, Jacques Hubert, Andres Mendez-Vazquez, C. Daul

引用次数: 4

Long-Term Exploration in Persistent MDPs 可持续发展目标的长期探索

Mexican International Conference on Artificial Intelligence Pub Date : 2021-09-21 DOI: 10.1007/978-3-030-89817-5_8

L. Ugadiarov, Alexey Skrynnik, A. Panov

引用次数: 1