2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)最新文献_第5页

Connected-Components-based Post-processing for Retinal Vessels Deep-Learning Segmentation 基于连接组件的视网膜血管深度学习分割后处理

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179039

Idris Dulau, Benoit Recur, C. Helmer, C. Delcourt, M. Beurton-Aimar

{"title":"Connected-Components-based Post-processing for Retinal Vessels Deep-Learning Segmentation","authors":"Idris Dulau, Benoit Recur, C. Helmer, C. Delcourt, M. Beurton-Aimar","doi":"10.1109/ICPRS58416.2023.10179039","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179039","url":null,"abstract":"The analysis of fundus images may reflect systemic and cerebral vascular status through a non-invasive, rapid, and cost-effective method. Accurate characterization of the retinal vessels is critical for this status assessment. Medical professionals can perform diagnosis on measurements extracted from the retinal vessels, which are identified through segmentation. Supervised-Learning is used to perform this segmentation task and has been shown to produce higher-quality results compared to traditional methods. However, the Supervised-Learning-based binary method leads to segmentations with multiple Connected Components (CC). Amongst these components, some are disconnected retinal vessels (mentioned as branches), others are artifacts. Artifacts are disconnected miss-classified components resulting from the Supervised-Learning segmentation and that should be removed. Conversely, branches should be kept and further re-connected as they are anatomically supposed to be connected. In this study, we propose a Connected-Components-based post-processing procedure to remove artifacts while preserving the most possible amount of branches. Our methodology involves a relative threshold to cluster the CC based on their areas. We also introduce a useful evaluation metric for the segmentations in the case of measurements extractions on retinal vessels. Over 615 predicted segmentations from six datasets, we improved the dice by a substantial 0.062 leading from 0.782 to 0.844. In conclusion, our method has the potential to significantly enhance the usability and reliability of retinal vessels segmentations, making it a valuable tool for medical professionals in the assessment of systemic and cerebral vascular status. Our work also provides useful insights for future research in this area, especially to address the re-connection of the remaining branches.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"31 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122074059","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

Morphological characterization of compact aggregates using image analysis and a geometrical stochastic 3D model 使用图像分析和几何随机三维模型的致密聚集体的形态特征

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179036

L. Théodon, C. Coufort-Saudejaud, Ali Hamieh, J. Debayle

{"title":"Morphological characterization of compact aggregates using image analysis and a geometrical stochastic 3D model","authors":"L. Théodon, C. Coufort-Saudejaud, Ali Hamieh, J. Debayle","doi":"10.1109/ICPRS58416.2023.10179036","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179036","url":null,"abstract":"The 3D morphological characteristics of size and shape of latex nanoparticle aggregates have a major impact on the quality of these powders and their performances such as flowability/processability in an industrial context. Most of the time the morphological characterization of these aggregates is only possible from the analysis of projected images, which only allows to measure 2D features. In this paper, a method for the 3D morphological characterization of a compact aggregate from the analysis of a single image is proposed. This method is composed of three steps: the description of a stochastic geometric model, the acquisition of data from image analysis, and the fitting of the model parameters to the data using an optimization process. The method was validated using images of a calibrated 3D printed aggregate, and the results presented in the last section show discrepancies lower than 1% for 2D morphological characteristics and lower than 2% in most cases for 3D characteristics. The limitations of the model are discussed and suggestions for improvement are made in the conclusion.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122131810","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

DeepSIT: Deeply Supervised Framework for Image Translation on Breast Cancer Analysis 用于乳腺癌分析的深度监督图像翻译框架

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10178999

Maria Baldeon-Calisto, Francisco Rivera-Velastegui, Daniel Riofrío, Ricardo Flores-Moyano, Noel Pérez, D. Benítez

{"title":"DeepSIT: Deeply Supervised Framework for Image Translation on Breast Cancer Analysis","authors":"Maria Baldeon-Calisto, Francisco Rivera-Velastegui, Daniel Riofrío, Ricardo Flores-Moyano, Noel Pérez, D. Benítez","doi":"10.1109/ICPRS58416.2023.10178999","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10178999","url":null,"abstract":"Image translation networks are deep learning models that can convert an image from one domain to another while preserving the semantic content. These networks are helpful in the medical field for noise reduction, reconstruction, and modality conversion. In this work, we propose DeepSIT, a deeply supervised framework for image translation. DeepSIT is a conditional generative adversarial network composed of a deeply supervised U-Net generator network and four PatchGAN discriminator networks. The generator performs the translation task while the discriminators judge the quality of the generated images. Unlike other works, the generator has four output layers located in the final and intermediate layers of the network. Each output layer generates a synthetic image, which is evaluated using a pixel-wise L1 loss function. Furthermore, the four discriminator networks receive a predicted image from an output layer to judge the quality of the translation at different scales. A promising application of image translation is the generation of immunohistochemical (IHC) images from Hematoxylin and Eosin (HE) images for breast cancer diagnosis. The proposed framework is evaluated in the latter tasks using the BCI Image Generation Grand Challenge dataset. DeepSIT achieves first place in the post-challenge leaderboard with an average of 0.545 SSIM and 18.037 PSNR in the test set.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128376329","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

From Synthetic Data to Real Palm Vein Identification: a Fine-Tuning Approach 从合成数据到真实手掌静脉识别:一种微调方法

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179042

R. Hernández-García, E. Salazar-Jurado, R. J. Barrientos, F. M. Castro, Julián Ramos-Cózar, N. Guil

{"title":"From Synthetic Data to Real Palm Vein Identification: a Fine-Tuning Approach","authors":"R. Hernández-García, E. Salazar-Jurado, R. J. Barrientos, F. M. Castro, Julián Ramos-Cózar, N. Guil","doi":"10.1109/ICPRS58416.2023.10179042","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179042","url":null,"abstract":"Palm vein recognition has relevant advantages in comparison with most traditional biometrics, such as high security and recognition performance. In recent years, CNN-based models for vascular biometrics have improved the state-of-the-art, but they have the disadvantage of requiring a larger number of samples for training. In this context, the generation of synthetic databases is very effective for evaluating the performance of biometric systems. The present study proposes a new perspective of a transfer learning approach for palm vein recognition, evaluating the use of Synthetic-sPVDB and NS-PVDB synthetic databases for pre-training deep learning models and validating their performance on real databases. The proposed methodology comprises two different branches as inputs. Firstly, a synthetic database is used to train a CNN model, and in the second branch, a real database is used to finetune and evaluate the performance of the resulting pre-trained model. For the feature learning process, we implemented two end-to-end CNN architectures based on AlexNet and Resnet32. The experimental results on the most representative public datasets have shown the usefulness of using palm vein synthetic images for transfer learning, outperforming the state-of-the-art results.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125317367","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

Addressing Bias in Fine-Grained Classification Datasets: A Strategy for Reliable Evaluation 处理细粒度分类数据集中的偏差:一种可靠评估策略

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179055

Stefan Wolf, Jannik Koch, L. Sommer, Jürgen Beyerer

{"title":"Addressing Bias in Fine-Grained Classification Datasets: A Strategy for Reliable Evaluation","authors":"Stefan Wolf, Jannik Koch, L. Sommer, Jürgen Beyerer","doi":"10.1109/ICPRS58416.2023.10179055","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179055","url":null,"abstract":"The high specificity of classes in fine-grained clas-sification tasks leads to a small number of images per class in the common research datasets. Thus, the intra-class variance, such as differences in vehicle colors for fine-grained vehicle classification, may not be properly represented. Consequently, there can be a heavy bias in regards to certain attributes, such as vehicle colors, leading to unintended information leakage to the classifier which can relate the vehicle color to a vehicle model. This, in turn, can result in misleading accuracy estimations as we show in this study. To address this issue, we propose a method to quantify the bias of a train-test split in regard to a specific attribute, providing a metric for the expressiveness of the results. To prevent the bias from resulting in misleading accuracy estimations, we apply a simple splitting scheme that separates the manifestations of the attribute. This split prevents the model from exploiting features which are unrelated to the actual task at hand, leading to more accurate estimations of the model's real-world performance and generalization ability. We demonstrate the effectiveness of our method by examining the vehicle color bias in fine-grained vehicle classification datasets. Our results show that the strong performance of current methods, which render this task as practically solved, is largely due to the exploitation of this bias. We can cope with it to some degree by transforming the images to grayscale, partly restoring the performance of the original split. However, the accuracies are still far lower than indicated by the original split. Additionally, we demonstrate that the original random train-test splits of datasets may show higher accuracies for poorlier generalizing methods, which renders experimentation to find better methods misleading. Therefore, better splitting schemes, such as our attribute-based splitting scheme, are required to obtain trustworthy results in experiments.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125739688","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

Pre or Post-Softmax Scores in Gradient-based Attribution Methods, What is Best? 基于梯度归因方法的softmax前后评分，哪个是最好的?

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-06-22 DOI: 10.1109/ICPRS58416.2023.10179032

Miguel A. Lerma, Mirtha Lucas

引用次数: 1

An Algebraic Approach to the Solutions of the Open Shop Scheduling Problem 开放式车间调度问题求解的代数方法

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-05-08 DOI: 10.1109/ICPRS58416.2023.10179081

A. M. Cañadas, Danna Odette Moreno Mendez, J. C. R. Rojas, Juan David Hormaza Pantoja

引用次数: 0

Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football 通过奖励塑造在多智能体学习中嵌入上下文信息:以谷歌足球为例

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-03-25 DOI: 10.1109/ICPRS58416.2023.10179030

Chaoyi Gu, V. D. Silva, Corentin Artaud, Rafael Pina

{"title":"Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football","authors":"Chaoyi Gu, V. D. Silva, Corentin Artaud, Rafael Pina","doi":"10.1109/ICPRS58416.2023.10179030","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179030","url":null,"abstract":"Artificial Intelligence has been used to help human complete difficult tasks in complicated environments by providing optimized strategies for decision-making or replacing the manual labour. In environments including multiple agents, such as football, the most common methods to train agents are Imitation Learning and Multi-Agent Reinforcement Learning (MARL). However, the agents trained by Imitation Learning cannot outperform the expert demonstrator, which makes humans hardly get new insights from the learnt policy. Besides, MARL is prone to the credit assignment problem. In environments with sparse reward signal, this method can be inefficient. The objective of our research is to create a novel reward shaping method by embedding contextual information in reward function to solve the aforementioned challenges. We demonstrate this in the Google Research Football (GRF) environment. We quantify the contextual information extracted from game state observation and use this quantification together with original sparse reward to create the shaped reward. The experiment results in the GRF environment prove that our reward shaping method is a useful addition to state-of-the-art MARL algorithms for training agents in environments with sparse reward signal.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121336","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