Martinez-Herrera, S.E.[Sergio E.],
Benezeth, Y.[Yannick],
Boffety, M.[Matthieu],
Emile, J.F.[Jean-François],
Marzani, F.[Franck],
Lamarque, D.[Dominique],
Goudail, F.[François],
Identification of precancerous lesions by multispectral gastroendoscopy,
SIViP(10), No. 3, March 2016, pp. 455-462.
Springer DOI
1602
BibRef
Iakovidis, D.K.,
Georgakopoulos, S.V.,
Vasilakakis, M.,
Koulaouzidis, A.,
Plagianakos, V.P.,
Detecting and Locating Gastrointestinal Anomalies Using Deep Learning
and Iterative Cluster Unification,
MedImg(37), No. 10, October 2018, pp. 2196-2210.
IEEE DOI
1810
Feature extraction, Training, Lesions, Image segmentation,
Image color analysis, Endoscopes, Gastrointestinal tract,
machine learning
BibRef
Wang, X.,
Seetohul, V.,
Chen, R.,
Zhang, Z.,
Qian, M.,
Shi, Z.,
Yang, G.,
Mu, P.,
Wang, C.,
Huang, Z.,
Zhou, Q.,
Zheng, H.,
Cochran, S.,
Qiu, W.,
Development of a Mechanical Scanning Device With High-Frequency
Ultrasound Transducer for Ultrasonic Capsule Endoscopy,
MedImg(36), No. 9, September 2017, pp. 1922-1929.
IEEE DOI
1709
biomedical ultrasonics, endoscopes,
gastrointestinaltract,
BibRef
Li, G.,
Li, H.,
Duan, X.,
Zhou, Q.,
Zhou, J.,
Oldham, K.R.,
Wang, T.D.,
Visualizing Epithelial Expression in Vertical and Horizontal Planes
With Dual Axes Confocal Endomicroscope Using Compact Distal Scanner,
MedImg(36), No. 7, July 2017, pp. 1482-1490.
IEEE DOI
1707
Biomedical optical imaging, Micromechanical devices, Mirrors,
Optical device fabrication, Optical imaging, Optical scattering,
Molecular and cellular imaging, endoscopy,
gastrointestinal tract, optical imaging, system, design
BibRef
Khan, M.A.[Mehshan Ahmed],
Khan, M.A.[Muhammad Attique],
Ahmed, F.[Fawad],
Mittal, M.[Mamta],
Goyal, L.M.[Lalit Mohan],
Hemanth, D.J.[D. Jude],
Satapathy, S.C.[Suresh Chandra],
Gastrointestinal diseases segmentation and classification based on
duo-deep architectures,
PRL(131), 2020, pp. 193-204.
Elsevier DOI
2004
Stomach diseases, Mask RCNN, CNN features, Feature selection
BibRef
Li, G.,
Duan, X.,
Lee, M.,
Birla, M.,
Chen, J.,
Oldham, K.R.,
Wang, T.D.,
Li, H.,
Ultra-Compact Microsystems-Based Confocal Endomicroscope,
MedImg(39), No. 7, July 2020, pp. 2406-2414.
IEEE DOI
2007
Mirrors, Instruments, Endoscopes, Micromechanical devices, Imaging,
Optical fibers, Optical imaging, endoscopy, system design,
gastrointestinal tract
BibRef
Yang, S.,
Lemke, C.,
Cox, B.F.,
Newton, I.P.,
Näthke, I.,
Cochran, S.,
A Learning-Based Microultrasound System for the Detection of
Inflammation of the Gastrointestinal Tract,
MedImg(40), No. 1, January 2021, pp. 38-47.
IEEE DOI
2012
Machine learning, Mice, Diseases, Acoustics, Imaging, Endoscopes,
Gastrointestinal tract, Computer-aided detection and diagnosis,
neural network
BibRef
Hu, H.Y.[Hui-Yi],
Zheng, W.F.[Wen-Fang],
Zhang, X.[Xu],
Zhang, X.S.[Xin-Sen],
Liu, J.Q.[Ji-Quan],
Hu, W.L.[Wei-Ling],
Duan, H.L.[Hui-Long],
Si, J.M.[Jian-Min],
Content-Based Gastric Image Retrieval Using Convolutional Neural
Networks,
IJIST(31), No. 1, 2021, pp. 439-449.
DOI Link
2102
clinical aided diagnosis, content-based image retrieval,
convolutional neural networks, gastric precancerous diseases, gastric-map
BibRef
Wang, L.S.[Lian-Sheng],
Jiao, Y.[Yudi],
Qiao, Y.[Ying],
Zeng, N.Y.[Nian-Yin],
Yu, R.S.[Rong-Shan],
A novel approach combined transfer learning and deep learning to
predict TMB from histology image,
PRL(135), 2020, pp. 244-248.
Elsevier DOI
2006
Gastrointestinal cancer, Tumor mutational burden,
Deep learning, Pathological images
BibRef
Mathialagan, P.[Prabhakaran],
Chidambaranathan, M.[Malathy],
Computer vision techniques for Upper Aero-Digestive Tract tumor
grading classification: Addressing pathological challenges,
PRL(144), 2021, pp. 42-53.
Elsevier DOI
2103
FR-PSO, SVM, Classification, Cancer, UADT, Machine Learning
BibRef
Wang, Q.[Qiong],
Li, Z.P.[Zhi-Peng],
Zhao, W.Q.[Wan-Qing],
Wu, H.[Hao],
Xie, F.[Fei],
Guan, Z.Y.[Zi-Yu],
Zhao, W.[Wei],
Enhanced three-dimensional U-Net with graph-based refining for
segmentation of gastrointestinal stromal tumours,
IET-CV(15), No. 8, 2021, pp. 549-560.
DOI Link
2110
BibRef
Guo, J.B.[Jian-Bin],
Wang, H.L.[Hao-Lin],
Xue, X.[Xingsi],
Li, M.T.[Meng-Ting],
Ma, Z.X.[Zhong-Xiong],
Real-time classification on oral ulcer images with residual network
and image enhancement,
IET-IPR(16), No. 3, 2022, pp. 641-646.
DOI Link
2202
BibRef
Li, S.[Sheng],
Cao, J.[Jing],
Yao, J.F.[Jia-Feng],
Zhu, J.H.[Jin-Hui],
He, X.X.[Xiong-Xiong],
Jiang, Q.R.[Qian-Ru],
Adaptive Aggregation with Self-Attention Network for Gastrointestinal
Image Classification,
IET-IPR(16), No. 9, 2022, pp. 2384-2397.
DOI Link
2206
BibRef
Chen, H.Y.[Hao-Yuan],
Li, C.[Chen],
Wang, G.[Ge],
Li, X.Y.[Xiao-Yan],
Rahaman, M.M.[Md Mamunur],
Sun, H.Z.[Hong-Zan],
Hu, W.M.[Wei-Ming],
Li, Y.X.[Yi-Xin],
Liu, W.L.[Wan-Li],
Sun, C.H.[Chang-Hao],
Ai, S.L.[Shi-Liang],
Grzegorzek, M.[Marcin],
GasHis-Transformer: A multi-scale visual transformer approach for
gastric histopathological image detection,
PR(130), 2022, pp. 108827.
Elsevier DOI
2206
Gastric histropathological image,
Multi-scale visual transformer, Image detection
BibRef
Ding, S.[Shuai],
Hu, S.[Shikang],
Li, X.J.[Xiao-Jian],
Zhang, Y.[Youtao],
Wu, D.D.[Desheng Dash],
Leveraging Multimodal Semantic Fusion for Gastric Cancer Screening
via Hierarchical Attention Mechanism,
SMCS(52), No. 7, July 2022, pp. 4286-4299.
IEEE DOI
2207
Cancer, Semantics, Medical diagnostic imaging, Feature extraction,
Decision making, Lesions, Analytical models,
multimodal fusion
BibRef
Aghanouri, M.[Mehrnaz],
Serej, N.D.[Nasim Dadashi],
Rabbani, H.[Hossein],
Adibi, P.[Peyman],
Automatic esophagus Z-line delineation in endoscopic images using a
new boundary linking method,
IET-IPR(16), No. 14, 2022, pp. 3842-3853.
DOI Link
2212
BibRef
Tamyalew, Y.[Yibeltal],
Salau, A.O.[Ayodeji Olalekan],
Ayalew, A.M.[Aleka Melese],
Detection and classification of large bowel obstruction from X-ray
images using machine learning algorithms,
IJIST(33), No. 1, 2023, pp. 158-174.
DOI Link
2301
CNN, GLCM, LBO, machine learning, segmentation, SVM, YOLOv3
BibRef
Xiao, Z.G.[Zhi-Guo],
Lu, J.[Jia],
Wang, X.K.[Xiao-Kun],
Li, N.F.[Nian-Feng],
Wang, Y.Y.[Yu-Ying],
Zhao, N.[Nan],
WCE-DCGAN: A data augmentation method based on wireless capsule
endoscopy images for gastrointestinal disease detection,
IET-IPR(17), No. 4, 2023, pp. 1170-1180.
DOI Link
2303
data augmentation,
deep convolutional generative adversarial networks (DCGAN),
WCE-DCGAN
BibRef
Wang, C.[Cong],
Gan, M.[Meng],
Few-shot segmentation for esophageal OCT images based on
self-supervised vision transformer,
IJIST(34), No. 2, 2024, pp. e23006.
DOI Link
2402
esophagus, image segmentation, optical coherence tomography,
self-supervised learning, vision transformer
BibRef
Nemani, P.[Praneeth],
Vadali, V.S.S.[Venkata Surya Sundar],
Medi, P.R.[Prathistith Raj],
Marisetty, A.[Ashish],
Vollala, S.[Satyanarayana],
Kumar, S.[Santosh],
Cross-modal hybrid architectures for gastrointestinal tract image
analysis: A systematic review and futuristic applications,
IVC(148), 2024, pp. 105068.
Elsevier DOI
2407
Segmentation, CNNs, Transformers, Generative AI,
Hybrid architectures, Dataset, GI-Tract
BibRef
Espantaleón-Pérez, R.[Ricardo],
Jiménez-Velasco, I.[Isabel],
Muñoz-Salinas, R.[Rafael],
Marín-Jiménez, M.J.[Manuel J.],
Empirical Study of Attention-based Models for Automatic Classification
of Gastrointestinal Endoscopy Images,
CAIP23(II:98-108).
Springer DOI
2312
BibRef
Wei, T.Y.[Ting-Yu],
Han, M.L.[Ming-Lun],
Liao, W.C.[Wei-Chih],
Yen, K.C.[Kuang-Chen],
Chen, S.J.[Shyh-Jye],
Chen, H.H.[Homer H.],
Endoscopic Feature Enhancement for Stomach 3D Reconstruction without
Dyeing,
ICIP23(1250-1254)
IEEE DOI
2312
BibRef
Maurício, J.[José],
Domingues, I.[Inês],
Deep Neural Networks to Distinguish Between Crohn's Disease and
Ulcerative Colitis,
IbPRIA23(533-544).
Springer DOI
2307
BibRef
Vázquez-González, L.[Lara],
Peña-Reyes, C.[Carlos],
Balsa-Castro, C.[Carlos],
Tomás, I.[Inmaculada],
Carreira, M.J.[María J.],
An Ensemble-based Phenotype Classifier to Diagnose Crohn's Disease from
16s RRNA Gene Sequences,
IbPRIA23(557-568).
Springer DOI
2307
BibRef
Zhang, J.[Jing],
Wen, T.[Tao],
He, T.[Tao],
Wang, X.Z.[Xiang-Zhou],
Hao, R.[Ruqian],
Liu, J.[Juanxiu],
Du, X.H.[Xiao-Hui],
Liu, L.[Lin],
Human Stools Classification for Gastrointestinal Health based on an
Improved ResNet18 Model with Dual Attention Mechanism,
CVPM22(2095-2102)
IEEE DOI
2210
Image segmentation, Head, Shape, Image color analysis, Convolution,
Feature extraction, Multitasking
BibRef
Jha, D.[Debesh],
Ali, S.[Sharib],
Emanuelsen, K.[Krister],
Hicks, S.A.[Steven A.],
Thambawita, V.[Vajira],
Garcia-Ceja, E.[Enrique],
Riegler, M.A.[Michael A.],
de Lange, T.[Thomas],
Schmidt, P.T.[Peter T.],
Johansen, H.D.[Håvard D.],
Johansen, D.[Dag],
Halvorsne, P.[Pål],
Kvasir-instrument: Diagnostic and Therapeutic Tool Segmentation Dataset
in Gastrointestinal Endoscopy,
MMMod21(II:218-229).
Springer DOI
2106
BibRef
Studer, L.[Linda],
Wallau, J.[Jannis],
Dawson, H.[Heather],
Zlobec, I.[Inti],
Fischer, A.[Andreas],
Classification of Intestinal Gland Cell-Graphs Using Graph Neural
Networks,
ICPR21(3636-3643)
IEEE DOI
2105
Deep learning, Message passing, Glands, Morphology,
Graph neural networks, Mirrors
BibRef
Chheda, T.[Tejas],
Iyer, R.[Rithvika],
Koppaka, S.[Soumya],
Kalbande, D.[Dhananjay],
Gastrointestinal Tract Anomaly Detection from Endoscopic Videos Using
Object Detection Approach,
ISVC20(II:494-505).
Springer DOI
2103
BibRef
He, Q.[Qi],
Bano, S.[Sophia],
Stoyanov, D.[Danail],
Zuo, S.Y.[Si-Yang],
Hybrid Loss with Network Trimming for Disease Recognition in
Gastrointestinal Endoscopy,
EndoTect20(299-306).
Springer DOI
2103
BibRef
Galdran, A.[Adrian],
Carneiro, G.[Gustavo],
Ballester, M.A.G.[Miguel A. González],
A Hierarchical Multi-task Approach to Gastrointestinal Image Analysis,
EndoTect20(275-282).
Springer DOI
2103
BibRef
Togo, R.,
Ogawa, T.,
Haseyama, M.,
Multimodal Image-to-Image Translation for Generation of Gastritis
Images,
ICIP20(2466-2470)
IEEE DOI
2011
Cancer, Inspection, Blood, X-ray imaging, Task analysis,
Indexes, Image-to-image translation,
gastritis
BibRef
Togo, R.,
Ishihara, K.,
Ogawa, T.,
Haseyama, M.,
Anonymous Gastritis Image Generation via Adversarial Learning from
Gastric X-Ray Images,
ICIP18(2082-2086)
IEEE DOI
1809
X-ray imaging, Biomedical imaging, Image recognition,
Stomach, Generative adversarial networks, gastritis
BibRef
Li, G.,
Togo, R.,
Ogawa, T.,
Haseyama, M.,
Soft-Label Anonymous Gastric X-Ray Image Distillation,
ICIP20(305-309)
IEEE DOI
2011
X-ray imaging, Medical diagnostic imaging, Training, Stomach,
Data privacy, Image coding, Medical image distillation,
gastric X-ray images
BibRef
Kanai, M.,
Togo, R.,
Ogawa, T.,
Haseyama, M.,
Gastritis Detection from Gastric X-Ray Images Via Fine-Tuning of
Patch-Based Deep Convolutional Neural Network,
ICIP19(1371-1375)
IEEE DOI
1910
Gastritis detection, convolutional neural network, fine-tuning,
gastric X-ray images
BibRef
Ishihara, K.[Kenta],
Ogawa, T.[Takahiro],
Haseyama, M.[Miki],
Detection of gastric cancer risk from X-ray images via patch-based
convolutional neural network,
ICIP17(2055-2059)
IEEE DOI
1803
BibRef
Earlier:
Helicobacter pylori infection detection from multiple x-ray images
based on combination use of support vector machine and multiple
kernel learning,
ICIP15(4728-4732)
IEEE DOI
1512
BibRef
Earlier:
Helicobacter pylori infection detection from multiple X-ray images
based on decision level fusion,
ICIP14(2769-2773)
IEEE DOI
1502
Biomedical imaging, Cancer, Endoscopes, Feature extraction,
Support vector machines, Training, X-ray imaging, Bag-of-Features,
gastric cancer risk detection.
Helicobacter pylori.
Cancer
BibRef
Trinh, D.H.,
Daul, C.,
Blondel, W.,
Lamarque, D.,
Mosaicing of Images with Few Textures and Strong Illumination
Changes: Application to Gastroscopic Scenes,
ICIP18(1263-1267)
IEEE DOI
1809
Lighting, Robustness, Image color analysis, Image sequences,
Estimation, Stomach, Kernel, Image mosaicing,
Medical endoscopy
BibRef
Diamantis, D.,
Iakovidis, D.K.,
Koulaouzidis, A.,
Investigating Cross-Dataset Abnormality Detection in Endoscopy with A
Weakly-Supervised Multiscale Convolutional Neural Network,
ICIP18(3124-3128)
IEEE DOI
1809
Feature extraction, Computer architecture, Training, Endoscopes,
Neurons, Convolutional neural networks, Gastrointestinal tract,
multiscale image analysis
BibRef
Vu, H.[Hai],
Echigo, T.[Tomio],
Imura, Y.[Yuma],
Yanagawa, Y.[Yukiko],
Yagi, Y.S.[Yasu-Shi],
Segmenting Reddish Lesions in Capsule Endoscopy Images Using a
Gastrointestinal Color Space,
ICPR14(3263-3268)
IEEE DOI
1412
Educational institutions
BibRef
Minami, Y.[Yoshitaka],
Ohnishi, T.[Takashi],
Kawahira, H.[Hiroshi],
Haneishi, H.[Hideaki],
Fundamental Study on Intraoperative Quantification of Gastrointestinal
Viability by Transmission Light Intensity Analysis,
ICISP14(72-78).
Springer DOI
1406
BibRef
Martinez-Herrera, S.E.[Sergio E.],
Benezeth, Y.[Yannick],
Boffety, M.[Matthieu],
Emile, J.F.[Jean-François],
Marzani, F.[Franck],
Lamarque, D.[Dominique],
Goudail, F.[François],
Multispectral Endoscopy to Identify Precancerous Lesions in Gastric
Mucosa,
ICISP14(43-51).
Springer DOI
1406
BibRef
Kim, K.B.[Kwang-Baek],
Kim, S.S.[Sung-Shin],
Kim, G.H.[Gwang-Ha],
Analysis System of Endoscopic Image of Early Gastric Cancer,
ICIAR06(II: 547-558).
Springer DOI
0610
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Colonoscopy, Colon Cancer .