Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto‐Setälä, Katriina; Juhola, Martti

doi:10.1155/2016/3025057

Cited by 30 publications

(18 citation statements)

References 27 publications

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“…On the other hand, OVA and OVO are two approaches in Machine Learning to address the problem of multiclassification [31, 32]. These approaches are widely used in the diagnosis of multiple subtypes in other conditions [33–35]. These experiments provide insight into how well one subtype distinguishes from another and also how well one subtype distinguishes against the others.…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods

Canul-Reich

Hernández-Torruco

Chávez-Bosquez

et al. 2018

Computational Intelligence and Neuroscience

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Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain–Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has barely been studied with computational techniques and few predictive models have been proposed. In a previous study, single classifiers were successfully used to build a predictive model. We believe that a predictive model is imperative to carry out adequate treatment in patients promptly. We designed three classification experiments: (1) using all four GBS subtypes, (2) One versus All (OVA), and (3) One versus One (OVO). These experiments use a real-world dataset with 129 instances and 16 relevant features. Besides, we compare five state-of-the-art ensemble methods against 15 single classifiers with 30 independent runs. Standard performance measures were used to obtain the best classifier in each experiment. Derived from the experiments, we conclude that Random Forest showed the best results in four GBS subtypes classification, no ensemble method stood out over the rest in OVA classification, and single classifiers outperformed ensemble methods in most cases in OVO classification. This study presents a novel predictive model for classification of four subtypes of Guillain–Barré syndrome. Our model identifies the best method for each classification case. We expect that our model could assist specialized physicians as a support tool and also could serve as a basis to improved models in the future.

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Section: Discussionmentioning

confidence: 99%

A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods

Canul-Reich

Hernández-Torruco

Chávez-Bosquez

et al. 2018

Computational Intelligence and Neuroscience

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show abstract

“…AlexNet is proposed in 2012, when compared with baggier CNN model, the AlexNet won in the most challenging ImageNet experiment for graphic object identification [18]. AlexNet reached a higher identification accuracy compared to all the machine learning algorithms and computer vision methods.…”

Section: Alexnetmentioning

confidence: 99%

“…In ternary coding, the elements of the coding matrix belong to the set {-1,0,1}. In this approach values -1 and 1 have the same purpose as in binary coding but now 0 means that this specific class is excluded from the training of an individual binary classifier [18].…”

Section: Error-correcting Output Codes (Ecoc) Multiclass Modelmentioning

confidence: 99%

“…There are numerous strategies of how to construct the coding matrix. We chose one-vs-all (OVA) strategies [18] which uses binary coding and each one of the binary classifiers separates one class from the rest. Hence, the number of binary classifiers is only (M) where M is the number of classes.…”

Section: Error-correcting Output Codes (Ecoc) Multiclass Modelmentioning

confidence: 99%

“…The distance between two Jdimensional vectors x and y is calculated by Euclidean distance that appear in the Eq. (4) where J is the number of variables then select the 5 nearest neighbors and for distance weighting functions we chose equal weighting [18].…”

Section: Error-correcting Output Codes (Ecoc) Multiclass Modelmentioning

confidence: 99%

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Dorsal Hand Vein Recognition by Convolutional Neural Networks: Feature Learning and Transfer Learning Approaches

Al-johania¹,

Elrefaei²

2019

IJIES

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In this paper, we propose a dorsal hand vein recognition system using Convolutional Neural Network (CNN). This system is automatically learned how to extract features from original image without preprocessing. The proposed system has two approaches: the first one is using the pre-trained CNN models (AlexNet, VGG16 and VGG19) for extracting features from 'fc6','fc7' and 'fc8' layers then using Error-Correcting Output Codes (ECOC) with Support Vector machine (SVM) and K-Nearest Neighbor (K-NN) algorithms for the classification. The second approach is using transfer learning with CNN (AlexNet, VGG16 and VGG19) models for both features' extraction and classification. The experiments applied on two different datasets: Dr. Badawi hand veins dataset that contains 500 image and BOSPPHORUS dorsal vein dataset that contains 1575 images. In the first approach experiment, the recognition accuracy of all models gives best result when features are extracted from 'fc6'. Also, the accuracy rate of the models that use ECOC with SVM for classification is higher than the models that use ECOC with KNN and the VGG19 model achieves better results in models that use ECOC with SVM. In the second approach experiment, the recognition accuracy for all models give best result when epoch number is 50 where Dr. Badawi dataset in VGG16 and AlexNet reaches to 100% recognition rate and BOSPPHORUS dataset reaches to 99.25 % recognition rate in VGG19. Finally, the discussion concluded that using transfer learning is giving more accuracy rate than using the pre-trained CNN models for extracting features.

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AI‐organoid integrated systems for biomedical studies and applications

Maramraju,

Kowalczewski,

Kaza

et al. 2024

Bioengineering & Transla Med

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In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)‐derived organoids. Stem cell‐derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error‐prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid‐based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label‐free organoid recognition, and three‐dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI‐organoid integration, focusing on the establishment of reliable AI model decision‐making processes and the standardization of organoid research.

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Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

Cited by 30 publications

References 27 publications

A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods

A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods

Dorsal Hand Vein Recognition by Convolutional Neural Networks: Feature Learning and Transfer Learning Approaches

AI‐organoid integrated systems for biomedical studies and applications

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