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Randomized Tree Ensembles for Object Detection in Computational Pathology [chapter]

Thomas J. Fuchs, Johannes Haybaeck, Peter J. Wild, Mathias Heikenwalder, Holger Moch, Adriano Aguzzi, Joachim M. Buhmann
2009 Lecture Notes in Computer Science  
Randomized tree ensembles for object detection in computational pathology. In: Bebis, G [et al.].  ...  (ii) we present an ensemble learning algorithm based on randomized trees, which can cope with exceptionally high dimensional feature spaces in an efficient manner.  ...  Multiple Object Detection For multiple object detection in a gray scale image every location on a grid with step with δ is considered as an independent sample s which is classified by the ensemble.  ... 
doi:10.1007/978-3-642-10331-5_35 fatcat:fopsjul5q5clpinz7fsy5y4nz4

Classification of Endomicroscopic Images of the Lung Based on Random Subwindows and Extra-Trees

Chesner Desir, Caroline Petitjean, Laurent Heutte, Mathieu Salaun, Luc Thiberville
2012 IEEE Transactions on Biomedical Engineering  
The lack of expertise currently available on these images has first led us to choose a generic approach, based on pixel-value description of randomly extracted subwindows and decision tree ensemble for  ...  In addition, we introduce a rejection mechanism on the classifier output to prevent non detection errors.  ...  ACKNOWLEDGEMENTS The authors would like to thank the Ligue Contre le Cancer for supporting Chesner Désir.  ... 
doi:10.1109/tbme.2012.2204747 pmid:22907955 fatcat:wlpfcnfcnvgmrjggy4d7dgaoc4

Decision Tree Ensemble Techniques To Predict Thyroid Disease

2019 International journal of recent technology and engineering  
Decision tree provides help in making decision for very complex and large dataset. Decision tree techniques are used for gathering knowledge.  ...  In the proposed experiment real data from 499 thyroid patients were used classifications algorithms in predicting whether thyroid detected or not detected on the basis of T3, T4 and TSH experimental values  ...  ACKNOWLEDGEMENTS The author is grateful to Veer Bahadur Singh Purvanchal University Jaunpur, Uttar Pradesh, for providing financial support to work as Post Doctoral Research Fellowship.  ... 
doi:10.35940/ijrte.c6727.098319 fatcat:mcetindesreg3i5o33sxscgzga

Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest

Khin Win, Somsak Choomchuay, Kazuhiko Hamamoto, Manasanan Raveesunthornkiat
2018 Applied Sciences  
This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images.  ...  A double-strategy random forest was performed as an ensemble feature selector to select the most relevant features, and an ensemble classifier to differentiate between overlapping nuclei and single ones  ...  We also immensely grateful to Department of Pathology, Faculty of Medicine, Srinakharinwirot University, Thailand, for providing the datasets and insight and expertise that greatly assisted the research  ... 
doi:10.3390/app8091608 fatcat:akwccppqjjd55okrkdmizycr3e

Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model

Andres Bustamante-Arias, Abbas Cheddad, Julio Cesar Jimenez-Perez, Alejandro Rodriguez-Garcia
2021 Photonics  
A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group).  ...  Once all images were analyzed, representative areas from every digital image were also extracted, processed and analyzed for a statistical feature comparison between healthy and pathologic corneas.  ...  Acknowledgments: Susana Imperial Sauceda for corneal SD-OCT analyses. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/photonics8040118 fatcat:hvmiqwv6nfhevp5dsmgwqewxrm

Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

Pankaj Bhowmik, Dept. of Computer Science & Engineering, Hajee Mohammad Danesh Science and Technology University, Bangladesh, Pulak Chandra Bhowmik, U. A. Md. Ehsan Ali, Md. Sohrawordi
2021 International Journal of Information Technology and Computer Science  
The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier  ...  This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health  ...  Since the base learning algorithm Random Forest came up with the maximum outcome in validation, it is set as the meta classifier for the ensemble learning model. B.  ... 
doi:10.5815/ijitcs.2021.05.03 fatcat:i542nt42mfdihdd657dahsob7u

Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees

Raphaël Marée, Pierre Geurts, Louis Wehenkel
2009 IPSJ Transactions on Computer Vision and Applications  
We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted  ...  The approach is quantitatively evaluated on various types of images and achieves high recognition rates despite its conceptual simplicity and computational efficiency.  ...  In this paper, we propose to look at ensembles of totally randomized trees 7) for indexing (random) local patches.  ... 
doi:10.2197/ipsjtcva.1.46 fatcat:upzwgf5uvrcifds5oequqghuqa

A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients

Himer Avila-George, Miguel De-la-Torre, Wilson Castro, Danny Dominguez, Josué E. Turpo-Chaparro, Jorge Sánchez-Garcés
2021 Applied Sciences  
According to computer simulations, the proposed classification approach using XGBoost provided higher classification performance than other ensemble approaches; the resulting decision tree can be employed  ...  Computer-aided diagnosis is a research area of increasing interest in third-level pediatric hospital care.  ...  Rajagopal [25] Voice pathology detection The authors presented a method combining density clustering and support vector machines for voice pathology detection.  ... 
doi:10.3390/app11083529 fatcat:46n4vnse3jarbaz74rhwdocl5q

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Amal fouad Fouad, Beni Suef University, Hossam Moftah, Hesham Hefny, Beni Suef University, Cairo University
2020 International Journal of Intelligent Engineering and Systems  
In the real-world, object detection and classification face numerous challenges. The object has a large variation in appearances.  ...  The test image is matched with its learned class by performing a Bagging ensemble learning classifier. Bagging achieves 96.4% in average accuracy but when Boosting is used, it achieves 95.8%.  ...  HOG are features descriptors used in computer vision and image processing for object detection purpose [20, 21] .  ... 
doi:10.22266/ijies2020.0430.05 fatcat:eqak52mwv5cqbgn5dwsp7qtizi

Lifelong Machine Learning and root cause analysis for large-scale cancer patient data

Gautam Pal, Xianbin Hong, Zhuo Wang, Hongyi Wu, Gangmin Li, Katie Atkinson
2019 Journal of Big Data  
Conclusion: We propose an ensemble framework of stream and batch data for incremental lifelong learning.  ...  To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) model.  ...  Acknowlegements The authors thank the anonymous reviewers for their helpful suggestions and comments. Authors' contributions All mentioned authors contribute to the elaboration of the paper.  ... 
doi:10.1186/s40537-019-0261-9 fatcat:3bosbw7ukfg7lfrgzxss3taveu

Hybrid Ensemble Framework for Heart Disease Detection and Prediction

Elham Nikookar, Ebrahim Naderi
2018 International Journal of Advanced Computer Science and Applications  
However, there are almost no studies investigating capabilities of hybrid ensemble methods in building a detection and prediction model for heart disease.  ...  Data mining techniques have been widely used in clinical decision support systems for detection and prediction of various diseases.  ...  Random Tree and the results of best fuser classifier, i.e.  ... 
doi:10.14569/ijacsa.2018.090533 fatcat:bqxkvdmabneelmjs7ydjnmaq3q

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Andrew Lagree, Audrey Shiner, Marie Angeli Alera, Lauren Fleshner, Ethan Law, Brianna Law, Fang-I Lu, David Dodington, Sonal Gandhi, Elzbieta A. Slodkowska, Alex Shenfield, Katarzyna J. Jerzak (+2 others)
2021 Current Oncology  
This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study.  ...  Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes.  ...  Acknowledgments: The authors would like to thank Calvin Law, Jan Stewart, and Steve Russell for their continued research support.  ... 
doi:10.3390/curroncol28060366 pmid:34898544 pmcid:PMC8628688 fatcat:b5nbxvdnanchblqscx7h2yvcfi

Image Processing and Supervised Learning for Efficient Detection of Animal Diseases

J Akpojaro, Rotimi-Williams Bello
2020 Zenodo  
These challenges motivate the development of detection tools that can perform automatically using deep learning approaches such as convolutional neural networks which have received great acceptance in  ...  To our best knowledge, this work is one of the newest works carried out to facilitate the detection of animal diseases for the benefit of animal husbandry; this implies that more research efforts are ongoing  ...  So, equation (5) can be changed to: yi (w.xi + b) ≥ 1 ∀i (7) Random Forests Random forest is an ensemble tree-based learning algorithm ( Figure 6 ).  ... 
doi:10.5281/zenodo.4453852 fatcat:rdegfsm6bbg6jbzofef72lt7yi

Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification

M. Moazam Fraz, Waqas Jahangir, Saqib Zahid, Mian M. Hamayun, Sarah A. Barman
2017 Biomedical Signal Processing and Control  
In this paper, we have presented an ensemble classifier of bootstrapped decision trees for multiscale localization and segmentation of exudates in retinal fundus images.  ...  Several region based features are computed from candidate regions to train the ensemble classifier for classification of pixel as exudate and non-exudate region.  ...  Acknowledgements The Authors are thankful to the teams of DIARETDB1, e-Optha Ex, HEI-MED and Messidor for maintaining and keeping these databases alive and making it easily accessible for public to carry  ... 
doi:10.1016/j.bspc.2017.02.012 fatcat:mzp27jmxbvcalao7f76jqcdili

Fusing voice and query data for non-invasive detection of laryngeal disorders

E. Vaiciukynas, A. Verikas, A. Gelzinis, M. Bacauskiene, J. Minelga, M. Hållander, E. Padervinskis, V. Uloza
2015 Expert systems with applications  
Topic of the research is exploration and fusion of non-invasive measurements for an accurate detection of pathological larynx.  ...  Random forest (RF) is used here as a base-learner and also as a meta-learner for decision-level fusion.  ...  Random forest Random forest (RF) is a popular and efficient algorithm for classification, based on ensemble methods.  ... 
doi:10.1016/j.eswa.2015.07.001 fatcat:sety4i25lrezddoqmhbqw35xmq
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