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001-es BibID:BIBFORM102066
Első szerző:Hajdu András (matematikus, informatikus)
Cím:Combining Convolutional Neural Networks and Hand-Crafted Features in Medical Image Classification Tasks / Hajdu András, Harangi Balázs, Tóth János, Pap Melinda, Baran Ágnes
Dátum:2018
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Megjelenés:20th European Conference on Mathematics for Industry : Book of Abstracts / ed. Bodó Á., Fekete I., Izsák F., Maros G., Simon L. P.. - p. 299
További szerzők:Harangi Balázs (1986-) (programtervező matematikus) Tóth János (1984-) (programtervező matematikus) Pap Melinda (1982-) (informatikus) Baran Ágnes (1972-) (matematikus)
Pályázati támogatás:GINOP-2.1.7-15-2016-01641
GINOP
EFOP-3.6.2-16-2017-00015
EFOP
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2.

001-es BibID:BIBFORM116613
035-os BibID:(cikkazonosító)4730 (Scopus)85178934416
Első szerző:Harangi Balázs (programtervező matematikus)
Cím:Composing Diverse Ensembles of Convolutional Neural Networks by Penalization / Balazs Harangi, Agnes Baran, Marcell Beregi-Kovacs, Andras Hajdu
Dátum:2023
ISSN:2227-7390
Megjegyzések:Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNNs) can be created by forcing them to adjust their parameters during the training process to increase diversity in their decisions. As a new theoretical approach to reach this aim, we join the member neural architectures via a fully connected layer and insert a new correlation penalty term in the loss function to obstruct their similar operation. With this complementary term, we implement the standard guideline of ensemble creation to increase the members` diversity for CNNs in a more detailed and flexible way than similar existing techniques. As for applicability, we show that our approach can be efficiently used in various classification tasks. More specifically, we demonstrate its performance in challenging medical image analysis and natural image classification problems. Besides the theoretical considerations and foundations, our experimental findings suggest that the proposed technique is competitive. Namely, on the one hand, the classification rate of the ensemble trained in this way outperformed all the individual accuracies of the state-of-the-art member CNNs according to the standard error functions of these application domains. On the other hand, it is also validated that the ensemble members get more diverse and their accuracies are raised by adding the penalization term. Moreover, we performed a full comparative analysis, including other state-of-the-art ensemble-based approaches recommended for the same classification tasks. This comparative study also confirmed the superiority of our method, as it overcame the current solutions.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
ensemble-based network
penalization
loss function
image classification
diversity
Megjelenés:Mathematics. - 11 : 23 (2023), p. 1-19. -
További szerzők:Baran Ágnes (1972-) (matematikus) Beregi-Kovács Marcell (1995-) (Alkalmazott matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:ÚNKP-21-5-DE-485
Egyéb
Bolyai János Kutatási Ösztöndíj
MTA
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3.

001-es BibID:BIBFORM101999
Első szerző:Harangi Balázs (programtervező matematikus)
Cím:Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features / Harangi Balázs, Tóth János, Baran Ágnes, Hajdu András
Dátum:2019
Megjegyzések:Diabetic retinopathy (DR) and especially diabetic macular edema (DME) are common causes of vision loss as complications of diabetes. In this work, we consider an ensemble that organizes a convolutional neural network (CNN) and traditional hand-crafted features into a single architecture for retinal image classification. This approach allows the joint training of a CNN and the fine-tuning of the weights of handcrafted features to provide a final prediction. Our solution is dedicated to the automatic classification of fundus images according to the severity level of DR and DME. For an objective evaluation of our approach, we have tested its performance on the official test datasets of the IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Challenge 2: Diabetic Retinopathy Segmentation and Grading Challenge, section B. Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy and diabetic macular edema. As for our experimental results based on testing on the Indian Diabetic Retinopathy Image Dataset (IDRiD), the classification accuracies have been found to be 90.07% for the 5-class DR challenge, and 96.85% for the 3-class DME one.
ISBN:9781538613122
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
diabetic retinopathy screening
hand-crafted features
deep learning
ensemble learning
Megjelenés:41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / ed. Riccardo Barbieri. - p. 2699-2702. -
További szerzők:Tóth János (1984-) (programtervező matematikus) Baran Ágnes (1972-) (matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:EFOP-3.6.2-16-2017-00015
EFOP
EFOP-3.6.3-VEKOP-16-2017-00002
EFOP
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4.

001-es BibID:BIBFORM087061
035-os BibID:(cikkazonosító)102041 (WoS)000575379300006 (Scopus)85087589024
Első szerző:Harangi Balázs (programtervező matematikus)
Cím:Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support / Balazs Harangi, Agnes Baran, Andras Hajdu
Dátum:2020
ISSN:1746-8094 1746-8108
Megjegyzések:In this paper, we propose a deep convolutional neural network framework to classify dermoscopy images into seven classes. With taking the advantage that these classes can be merged into two (healthy/diseased) ones we can train a part of the network regarding this binary task only. Then, the confidences regarding the binary classification are used to tune the multi-class confidence values provided by the other part of the network, since the binary task can be solved more accurately. For both the classification tasks we used GoogLeNet Inception-v3, however, any CNN architectures could be applied for these purposes. The whole network is trained in the usual way, and as our experimental results on the skin lesion image classification show, the accuracy of the multi-class problem has been remarkably raised (by 7% considering the balanced multi-class accuracy) via embedding the more reliable binary classification outcomes.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Assisted learning
Deep learning
Ensemble learning
Skin lesion
Megjelenés:Biomedical Signal Processing and Control. - 62 (2020), p. 1-7. -
További szerzők:Baran Ágnes (1972-) (matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:EFOP-3.6.2-16-2017-00015
EFOP
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5.

001-es BibID:BIBFORM077188
Első szerző:Harangi Balázs (programtervező matematikus)
Cím:Classification of skin lesions using an ensemble of deep neural networks / Balazs Harangi, Agnes Baran, Andras Hajdu
Dátum:2018
Megjegyzések:Skin cancer is among the deadliest variants of cancer if not recognized and treated in time. This work focuses on the identification of this disease using an ensemble of state-of-the-art deep learning approaches. More specifically, we propose the aggregation of robust convolutional neural networks (CNNs) into one neural net architecture, where the final classification is achieved based on the weighted output of the member CNNs. Since our framework is realized within a single neural net architecture, all the parameters of the member CNNs and the weights applied in the fusion can be determined by backpropagation routinely applied for such tasks. The presented ensemble consists of the CNNs AlexNet, VGGNet, GoogLeNet, all of which have been won in subsequent years the most prominent worldwide image classification challenge ImageNet. For an objective evaluation of our approach, we have tested its performance on the official test database of the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection dedicated to skin cancer recognition. Our experimental studies show that the proposed approach is competitive in this field. Moreover, the ensemble-based approach outperformed all of its member CNNs.
ISBN:978-1-5386-3646-6
Tárgyszavak:Műszaki tudományok Informatikai tudományok könyvfejezet
Megjelenés:2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Gregg Suaning, Olaf Dossel. - p. 2575-2578. -
További szerzők:Baran Ágnes (1972-) (matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:EFOP-3.6.2-16-2017-00015
EFOP
GINOP-2.1.1-15-2015-00376
GINOP
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6.

001-es BibID:BIBFORM081291
035-os BibID:(cikkazonosító)101561 (WoS)000501405800010 (Scopus)85074152892
Első szerző:Porwal, Prasanna
Cím:IDRiD: Diabetic Retinopathy : segmentation and grading challenge / Prasanna Porwal, Samiksha Pachade, Manesh Kokare, Girish Deshmukh, Jaemin Son, Woong Bae, Lihong Liu, Jianzong Wang, Xinhui Liu, Liangxin Gao, TianBo Wu, Jing Xiao, Fengyan Wang, Baocai Yin, Yunzhi Wang, Gopichandh Danala, Linsheng He, Yoon Ho Choi, Yeong Chan Lee, Sang Hyuk Jung, Zhongyu Li, Xiaodan Sui, Junyan Wu, Xiaolong Li, Ting Zhou, János Tóth, Agnes Baran, Avinash Kori, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Xingzheng Lyu, Li Cheng, Qinhao Chu, Pengcheng Li, Xin Ji, Sanyuan Zhang, Yaxin Shen, Ling Dai, Oindrila Saha, Rachana Sathish, Tânia Melo, Teresa Araújo, Balázs Harangi, Bin Sheng, Ruogu Fang, Debdoot Sheet, Andras Hajdu, Yuanjie Zheng, Ana Maria Mendonça, Shaoting Zhang, Aurélio Campilho, Bin Zheng, Dinggang Shen, Luca Giancardo, Gwenolé Quellec, Fabrice Mériaudeau
Dátum:2020
ISSN:1361-8415
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Medical Image Analysis. - 59 (2020), p. 1-26. -
További szerzők:Pachade, Samiksha Kokare, Manesh Deshmukh, Girish Son, Jaemin Bae, Woong Liu, Lihong Wang, Jianzong Liu, Xinhui Gao, Liangxin Wu, TianBo Xiao, Jing Wang, Fengyan Yin, Baocai Wang, Yunzhi Danala, Gopichandh He, Linsheng Choi, Yoon Ho Lee, Yeong Chan Jung, Sang Hyuk Li, Zhongyu Sui, Xiaodan Wu, Junyan Li, Xiaolong Zhou, Ting Tóth János (1984-) (programtervező matematikus) Baran Ágnes (1972-) (matematikus) Kori, Avinash Chennamsetty, Sai Saketh Safwan, Mohammed Alex, Varghese Lyu, Xingzheng Cheng, Li Chu, Qinhao Li, Pengcheng Ji, Xin Zhang, Sanyuan Shen, Yaxin Dai, Ling Saha, Oindrila Sathish, Rachana Melo, Tânia Araújo, Teresa Harangi Balázs (1986-) (programtervező matematikus) Sheng, Bin Fang, Ruogu Sheet, Debdoot Hajdu András (1973-) (matematikus, informatikus) Zheng, Yuanjie Mendonça, Ana Maria Zhang, Shaoting Campilho, Aurélio Zheng, Bin Shen, Dinggang Giancardo, Luca Quellec, Gwenolé Mériaudeau, Fabrice
Pályázati támogatás:EFOP-3.6.2-16-2017-00015
EFOP
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