Skip to main content
Top

2021 | OriginalPaper | Chapter

11. Deep Learning-Driven Models for Endoscopic Image Analysis

Authors : Xiao Jia, Xiaohan Xing, Yixuan Yuan, Max Q.-H Meng

Published in: Advances in Artificial Intelligence, Computation, and Data Science

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The advent of video endoscopy has led to an increased interest in the development of computer-aided diagnosis (CAD) approaches. Many of these focus on the use of deep learning methods as a means of automatically identifying abnormalities during endoscopy to lessen the workload on doctors. In this chapter, we take two tasks in endoscopic image analysis as examples, to survey the state of the art, recent advances, and future directions of CAD applications, especially with regard to deep learning models. We introduce the fundamentals of deep learning-driven methods and elaborate on their success in areas such as endoscopic image classification, detection of abnormal regions, and lesion boundary segmentation.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Karkanis SA, Iakovidis DK, Maroulis DE, Magoulas GD, Theofanous NG (2000) Tumor recognition in endoscopic video images using artificial neural network architectures. In: Proceedings of the 26th Euromicro Conference. EUROMICRO 2000. Informatics: inventing the future, vol. 2. IEEE, New York, pp 423–429 Karkanis SA, Iakovidis DK, Maroulis DE, Magoulas GD, Theofanous NG (2000) Tumor recognition in endoscopic video images using artificial neural network architectures. In: Proceedings of the 26th Euromicro Conference. EUROMICRO 2000. Informatics: inventing the future, vol. 2. IEEE, New York, pp 423–429
2.
go back to reference Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M (2003) Computer-aided tumor detection in endoscopic video using color wavelet features. In: IEEE Transactions on information technology in biomedicine, vol 7. IEEE, New York, pp 141–152 Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M (2003) Computer-aided tumor detection in endoscopic video using color wavelet features. In: IEEE Transactions on information technology in biomedicine, vol 7. IEEE, New York, pp 141–152
3.
go back to reference Iakovidis DK, Koulaouzidis A (2015) Software for enhanced video capsule endoscopy: challenges for essential progress. In: Nature reviews gastroenterology & hepatology, vol 12. Nature Publishing Group, Berlin, pp 172–186 Iakovidis DK, Koulaouzidis A (2015) Software for enhanced video capsule endoscopy: challenges for essential progress. In: Nature reviews gastroenterology & hepatology, vol 12. Nature Publishing Group, Berlin, pp 172–186
4.
go back to reference Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. Curran Associates, Inc., Red Hook, NY, pp 1097–1105 Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. Curran Associates, Inc., Red Hook, NY, pp 1097–1105
5.
go back to reference Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 3431–3440 Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 3431–3440
6.
go back to reference Lei H, Han T, Zhou F, Yu Z, Qin J, Elazab A, Lei B (2018) A deeply supervised residual network for hep-2 cell classification via cross-modal transfer learning. In: Pattern recognition, vol 79. Elsevier, Amsterdam, pp 290–302 Lei H, Han T, Zhou F, Yu Z, Qin J, Elazab A, Lei B (2018) A deeply supervised residual network for hep-2 cell classification via cross-modal transfer learning. In: Pattern recognition, vol 79. Elsevier, Amsterdam, pp 290–302
7.
go back to reference Sarikaya D, Corso JJ, Guru KA (2017) Detection and localization of robotic tools in robot-assisted surgery videos using deep neural networks for region proposal and detection. In: IEEE transactions on medical imaging, vol 36. IEEE, New York, pp 1542–1549 Sarikaya D, Corso JJ, Guru KA (2017) Detection and localization of robotic tools in robot-assisted surgery videos using deep neural networks for region proposal and detection. In: IEEE transactions on medical imaging, vol 36. IEEE, New York, pp 1542–1549
8.
go back to reference Xu Y, Li Y, Liu M, Wang Y, Lai M, Eric I, Chang C (2016) Gland instance segmentation by deep multichannel side supervision. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 496–504 Xu Y, Li Y, Liu M, Wang Y, Lai M, Eric I, Chang C (2016) Gland instance segmentation by deep multichannel side supervision. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 496–504
9.
go back to reference Jia X, Meng MQ-H (2016) A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Orlando, FL, pp 639–642 Jia X, Meng MQ-H (2016) A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Orlando, FL, pp 639–642
10.
go back to reference Jia X, Meng MQ-H (2017) Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and cnn features. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, South Korea, pp 3154–3157 Jia X, Meng MQ-H (2017) Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and cnn features. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, South Korea, pp 3154–3157
11.
go back to reference Jia X, Meng MQ-H (2017) A study on automated segmentation of blood regions in wireless capsule endoscopy images using fully convolutional networks. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI). IEEE, Melbourne, pp 179–182 Jia X, Meng MQ-H (2017) A study on automated segmentation of blood regions in wireless capsule endoscopy images using fully convolutional networks. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI). IEEE, Melbourne, pp 179–182
12.
go back to reference Jia X, Cai L, Liu J, Dai W, Meng MQ-H (2016) GI bleeding detection in wireless capsule endoscopy images based on pattern recognition and a MapReduce framework. In: 2016 IEEE international conference on real-time computing and robotics (RCAR). IEEE, Cambodia, pp 266–271 Jia X, Cai L, Liu J, Dai W, Meng MQ-H (2016) GI bleeding detection in wireless capsule endoscopy images based on pattern recognition and a MapReduce framework. In: 2016 IEEE international conference on real-time computing and robotics (RCAR). IEEE, Cambodia, pp 266–271
13.
go back to reference Jia X, Mai X, Cui Y, Yuan Y, Xing X, Seo H, Xing L, Meng MQ-H (2020) Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. In: IEEE transactions on automation science and engineering. IEEE Jia X, Mai X, Cui Y, Yuan Y, Xing X, Seo H, Xing L, Meng MQ-H (2020) Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. In: IEEE transactions on automation science and engineering. IEEE
14.
go back to reference Jia X, Xing X, Yuan Y, Xing L, Meng MQ-H (2019) Wireless capsule endoscopy: a new tool for cancer screening in the colon with deep-learning-based polyp recognition. In: Proceedings of the IEEE, vol 108. IEEE, pp 178–197 Jia X, Xing X, Yuan Y, Xing L, Meng MQ-H (2019) Wireless capsule endoscopy: a new tool for cancer screening in the colon with deep-learning-based polyp recognition. In: Proceedings of the IEEE, vol 108. IEEE, pp 178–197
15.
go back to reference Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. Curran Associates, Inc., Red Hook, NY, pp 91–99 Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. Curran Associates, Inc., Red Hook, NY, pp 91–99
16.
go back to reference Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision. IEEE, Cambridge, MA, pp 1440–1448 Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision. IEEE, Cambridge, MA, pp 1440–1448
17.
go back to reference Bovik AC (2010) Handbook of image and video processing. Academic press, Cambridge Bovik AC (2010) Handbook of image and video processing. Academic press, Cambridge
18.
go back to reference Iddan G, Meron G, Glukhovsky A, Swain P (2000) Wireless capsule endoscopy. Nature 405:417. Nature Research Iddan G, Meron G, Glukhovsky A, Swain P (2000) Wireless capsule endoscopy. Nature 405:417. Nature Research
19.
go back to reference Van Gossum A, Munoz-Navas M, Fernandez-Urien I, Carretero C, Gay G, Delvaux M, Lapalus MG, Ponchon T, Neuhaus H, Philipper M, et al (2009) Capsule endoscopy versus colonoscopy for the detection of polyps and cancer. N Engl J Med 361:264–270. Mass Medical Soc Van Gossum A, Munoz-Navas M, Fernandez-Urien I, Carretero C, Gay G, Delvaux M, Lapalus MG, Ponchon T, Neuhaus H, Philipper M, et al (2009) Capsule endoscopy versus colonoscopy for the detection of polyps and cancer. N Engl J Med 361:264–270. Mass Medical Soc
20.
go back to reference Hwang S (2011) Bag-of-visual-words approach to abnormal image detection in wireless capsule endoscopy videos. In: International symposium on visual computing. Springer, Berlin, pp 320–327 Hwang S (2011) Bag-of-visual-words approach to abnormal image detection in wireless capsule endoscopy videos. In: International symposium on visual computing. Springer, Berlin, pp 320–327
21.
go back to reference Yu M (2002) \(\text{M2A}^{\text{ TM }}\) capsule endoscopy: a breakthrough diagnostic tool for small intestine imaging. Gastroenterol Nurs 25:24–27. LWW Yu M (2002) \(\text{M2A}^{\text{ TM }}\) capsule endoscopy: a breakthrough diagnostic tool for small intestine imaging. Gastroenterol Nurs 25:24–27. LWW
22.
go back to reference Fisher L, Krinsky ML, Anderson MA, Appalaneni V, Banerjee S, Ben-Menachem T, Cash BD, Decker GA, Fanelli RD, Friis C, et al (2010) The role of endoscopy in the management of obscure GI bleeding. Gastrointest Endosc 72:471–479. Elsevier Fisher L, Krinsky ML, Anderson MA, Appalaneni V, Banerjee S, Ben-Menachem T, Cash BD, Decker GA, Fanelli RD, Friis C, et al (2010) The role of endoscopy in the management of obscure GI bleeding. Gastrointest Endosc 72:471–479. Elsevier
23.
go back to reference Fu Y, Zhang W, Mandal M, Meng MQ-H (2014) Computer-aided bleeding detection in WCE video. IEEE J Biomed Health Inf 18:636–642. IEEE Fu Y, Zhang W, Mandal M, Meng MQ-H (2014) Computer-aided bleeding detection in WCE video. IEEE J Biomed Health Inf 18:636–642. IEEE
24.
go back to reference Mathew M, Gopi VP (2015) Transform based bleeding detection technique for endoscopic images. In: 2015 2nd international conference on electronics and communication systems (ICECS). IEEE, Piscataway, pp 1730–1734 Mathew M, Gopi VP (2015) Transform based bleeding detection technique for endoscopic images. In: 2015 2nd international conference on electronics and communication systems (ICECS). IEEE, Piscataway, pp 1730–1734
25.
go back to reference Ghosh T, Bashar SK, Alam MS, Wahid K, Fattah SA (2014) A statistical feature based novel method to detect bleeding in wireless capsule endoscopy images. In: 2014 international conference on informatics, electronics & vision (ICIEV). IEEE, Dhaka, pp 1–4 Ghosh T, Bashar SK, Alam MS, Wahid K, Fattah SA (2014) A statistical feature based novel method to detect bleeding in wireless capsule endoscopy images. In: 2014 international conference on informatics, electronics & vision (ICIEV). IEEE, Dhaka, pp 1–4
26.
go back to reference Yuan Y, Meng MQ-H (2015) Automatic bleeding frame detection in the wireless capsule endoscopy images. In: 2015 IEEE international conference on robotics and automation (ICRA). IEEE, Seattle, pp 1310–1315 Yuan Y, Meng MQ-H (2015) Automatic bleeding frame detection in the wireless capsule endoscopy images. In: 2015 IEEE international conference on robotics and automation (ICRA). IEEE, Seattle, pp 1310–1315
27.
go back to reference Yuan Y, Li B, Meng Q (2015) Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE J Biomed Health Inf 20:624–630. IEEE Yuan Y, Li B, Meng Q (2015) Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE J Biomed Health Inf 20:624–630. IEEE
28.
go back to reference Cancer Facts & Figures (2019) American cancer society. Atlanta, GA, USA Cancer Facts & Figures (2019) American cancer society. Atlanta, GA, USA
29.
go back to reference Colorectal Cancer Facts & Figures 2017–2019 (2017) American cancer society, Atlanta, GA, USA Colorectal Cancer Facts & Figures 2017–2019 (2017) American cancer society, Atlanta, GA, USA
30.
go back to reference Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9:283–293. Springer Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9:283–293. Springer
31.
go back to reference Bernal J, Sánchez FJ, Fernández-Esparrach G, Gil D, Rodríguez C, Vilariño F (2015) Wm-dova maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99–111. Elsevier Bernal J, Sánchez FJ, Fernández-Esparrach G, Gil D, Rodríguez C, Vilariño F (2015) Wm-dova maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99–111. Elsevier
32.
go back to reference Park SY, Sargent D (2016) Colonoscopic polyp detection using convolutional neural networks. In: Medical imaging 2016: computer-aided diagnosis. International society for optics and photonics, vol 9785, p 978528 Park SY, Sargent D (2016) Colonoscopic polyp detection using convolutional neural networks. In: Medical imaging 2016: computer-aided diagnosis. International society for optics and photonics, vol 9785, p 978528
33.
go back to reference Shin Y, Qadir HA, Aabakken L, Bergsland J, Balasingham I (2018) Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access 6:40950–40962. IEEE Shin Y, Qadir HA, Aabakken L, Bergsland J, Balasingham I (2018) Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access 6:40950–40962. IEEE
34.
go back to reference Yu L, Chen H, Dou Q, Qin J, Heng PA (2017) Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inf 21:65–75. IEEE Yu L, Chen H, Dou Q, Qin J, Heng PA (2017) Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inf 21:65–75. IEEE
35.
go back to reference Vázquez D, Bernal J, Sánchez FJ, Fernández-Esparrach G, López AM, Romero A, Drozdzal M, Courville A (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. J Healthcare Eng 2017. Hindawi Vázquez D, Bernal J, Sánchez FJ, Fernández-Esparrach G, López AM, Romero A, Drozdzal M, Courville A (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. J Healthcare Eng 2017. Hindawi
36.
go back to reference Zhang L, Dolwani S, Ye X (2017) Automated polyp segmentation in colonoscopy frames using fully convolutional neural network and textons. In: Annual conference on medical image understanding and analysis. Springer, Berlin, pp 707–717 Zhang L, Dolwani S, Ye X (2017) Automated polyp segmentation in colonoscopy frames using fully convolutional neural network and textons. In: Annual conference on medical image understanding and analysis. Springer, Berlin, pp 707–717
37.
go back to reference Brandao P, Zisimopoulos O, Mazomenos E, Ciuti G, Bernal J, Visentini-Scarzanella M, Menciassi A, Dario P, Koulaouzidis A, Arezzo A, et al (2018) Towards a computed-aided diagnosis system in colonoscopy: automatic polyp segmentation using convolution neural networks. J Med Robot Res 3:1840002. World Scientific Brandao P, Zisimopoulos O, Mazomenos E, Ciuti G, Bernal J, Visentini-Scarzanella M, Menciassi A, Dario P, Koulaouzidis A, Arezzo A, et al (2018) Towards a computed-aided diagnosis system in colonoscopy: automatic polyp segmentation using convolution neural networks. J Med Robot Res 3:1840002. World Scientific
38.
39.
go back to reference Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 39. IEEE, pp 2481–2495 Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 39. IEEE, pp 2481–2495
40.
go back to reference Xiao W-T, Chang L-J, Liu W-M (2018) Semantic segmentation of colorectal polyps with DeepLab and LSTM networks. In: 2018 IEEE international conference on consumer electronics-Taiwan (ICCE-TW). IEEE, pp 1–2 Xiao W-T, Chang L-J, Liu W-M (2018) Semantic segmentation of colorectal polyps with DeepLab and LSTM networks. In: 2018 IEEE international conference on consumer electronics-Taiwan (ICCE-TW). IEEE, pp 1–2
41.
go back to reference Wang P, Xiao X, Brown JRG, Berzin TM, Tu M, Xiong F, Hu X, Liu P, Song Y, Zhang D, et al (2018) Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nature Biomed Eng 2:741. Nature Publishing Group Wang P, Xiao X, Brown JRG, Berzin TM, Tu M, Xiong F, Hu X, Liu P, Song Y, Zhang D, et al (2018) Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nature Biomed Eng 2:741. Nature Publishing Group
42.
go back to reference He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 770–778 He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 770–778
43.
go back to reference Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1. IEEE, p 4 Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1. IEEE, p 4
44.
go back to reference Bernal J, Tajkbaksh N, Sánchez FJ, Matuszewski BJ, Chen H, Yu L, Angermann Q, Romain O, Rustad B, Balasingham I, et al (2017) Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge. In: IEEE transactions on medical imaging, vol 36. IEEE, pp 1231–1249 Bernal J, Tajkbaksh N, Sánchez FJ, Matuszewski BJ, Chen H, Yu L, Angermann Q, Romain O, Rustad B, Balasingham I, et al (2017) Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge. In: IEEE transactions on medical imaging, vol 36. IEEE, pp 1231–1249
45.
go back to reference Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, NV, pp 2921–2929 Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, NV, pp 2921–2929
46.
go back to reference Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Honolulu, HI, pp 2881–2890 Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Honolulu, HI, pp 2881–2890
47.
go back to reference Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV). Springer, Berlin, pp 801–818 Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV). Springer, Berlin, pp 801–818
48.
go back to reference Zhou B, Li Y, Wang J (2018) A weakly supervised adaptive densenet for classifying thoracic diseases and identifying abnormalities. arXiv:1807.01257 Zhou B, Li Y, Wang J (2018) A weakly supervised adaptive densenet for classifying thoracic diseases and identifying abnormalities. arXiv:​1807.​01257
49.
go back to reference Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Boston, MA, pp 2625–2634 Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Boston, MA, pp 2625–2634
50.
go back to reference Yueming J, Qi D, Hao C, Yu L, Jing Q, Fu C-W, Pheng-Ann H (2017) Sv-rcnet: workflow recognition from surgical videos using recurrent convolutional network. IEEE Trans Med Imag. IEEE Yueming J, Qi D, Hao C, Yu L, Jing Q, Fu C-W, Pheng-Ann H (2017) Sv-rcnet: workflow recognition from surgical videos using recurrent convolutional network. IEEE Trans Med Imag. IEEE
51.
go back to reference Xing X, Yuan Y, Meng MQ-H (2020) Zoom in lesions for better diagnosis: attention guided deformation network for WCE image classification. IEEE Trans Med Imag Xing X, Yuan Y, Meng MQ-H (2020) Zoom in lesions for better diagnosis: attention guided deformation network for WCE image classification. IEEE Trans Med Imag
52.
go back to reference Guo X, Yuan Y (2020) Semi-supervised WCE image classification with adaptive aggregated attention. Med Image Anal 64:101733 Guo X, Yuan Y (2020) Semi-supervised WCE image classification with adaptive aggregated attention. Med Image Anal 64:101733
Metadata
Title
Deep Learning-Driven Models for Endoscopic Image Analysis
Authors
Xiao Jia
Xiaohan Xing
Yixuan Yuan
Max Q.-H Meng
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-69951-2_11

Premium Partner