Oesophagus Achalasia Diagnosis from Esophagoscopy Based on a Serial Multi-scale Network

References

• Akobeng AK. 2007. Understanding diagnostic tests 1: sensitivity, specificity and predictive values. Acta Paediatr. 96(3):338–341. doi:10.1111/j.1651-2227.2006.00180.x.
   Google Scholar
• Barbieri LA, Hassan C, Rosati R, Romario UF, Correale L, Repici A. 2015. Systematic review and meta-analysis: efficacy and safety of POEM for achalasia. United Eur Gastroenterol J. 3(4):325–334. doi:10.1177/2050640615581732.
   Google Scholar
• Boeckxstaens GE, Zaninotto G, Richter JE. 2014. Achalasia. Lancet. 383(9911):83–93. doi:10.1016/S0140-6736(13)60651-0.
   Google Scholar
• Chen Q, Wang Y, Yang T, Zhang X, Cheng J, Sun J. 2021. You only look one-level feature. CoRR. abs/2103.09460. https://arxiv.org/abs/2103.09460.
   Google Scholar
• Cummins G, Cox BF, Ciuti G, Anbarasan T, Desmulliez MP, Cochran S, Steele R, Plevris JN, Koulaouzidis A. 2019. Gastrointestinal diagnosis using non-white light imaging capsule endoscopy. Nat Rev Gastroenterol Hepatol. 16(7):429–447. doi:10.1038/s41575-019-0140-z.
   Google Scholar
• DeVries T, Taylor GW. 2017. Improved regularization of convolutional neural networks with cutout. CoRR. abs/1708.04552.
   Google Scholar
• Ding X, Zhang X, Han J, Ding G. 2022. Scaling up your kernels to 31x31: Revisiting large kernel design in CNNs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans (LA). p. 11963–11975.
   Google Scholar
• Fadnavis S. 2014. Image interpolation techniques in digital image processing: an overview. Int J Eng Res Appl. 4(10):70–73.
   Google Scholar
• Gennaro N, Portale G, Gallo C, Rocchietto S, Caruso V, Costantini M, Salvador R, Ruol A, Zaninotto G. 2011. Esophageal achalasia in the Veneto region: epidemiology and treatment. J Gastrointestinal Surg. 15(3):423–428. doi:10.1007/s11605-010-1392-7.
   Google Scholar
• Gulli A, Pal S. 2017. Deep learning with Keras. Birmingham (UK): Packt Publishing Ltd.
   Google Scholar
• 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, Las Vegas (NV). p. 770–778.
   Google Scholar
• Huang G, Liu Z, Weinberger KQ. 2016. Densely connected convolutional networks. CoRR. abs/1608.06993. http://arxiv.org/abs/1608.06993.
   Google Scholar
• Ilse M, Tomczak JM, Welling M. 2018. Attention-based deep multiple instance learning. CoRR. abs/1802.04712. http://arxiv.org/abs/1802.04712.
   Google Scholar
• Inoue H, Minami H, Kobayashi Y, Sato Y, Kaga M, Suzuki M, Satodate H, Odaka N, Itoh H, Kudo S. 2010. Peroral endoscopic myotomy (POEM) for esophageal achalasia. Endoscopy. 42(04):265–271. doi:10.1055/s-0029-1244080.
   Google Scholar
• Ioffe S, Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, Lille, France. PMLR. p. 448–456.
   Google Scholar
• Kessy A, Lewin A, Strimmer K. 2018. Optimal whitening and decorrelation. Am Stat. 72(4):309–314. doi:10.1080/00031305.2016.1277159.
   Google Scholar
• Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D. 2020. Supervised contrastive learning. Adv Neural Inf Process Syst. 33:18661–18673.
   Google Scholar
• Kuznetsov K, Lambert R, Rey JF. 2006. Narrow-band imaging: potential and limitations. Endoscopy. 38(01):76–81. doi:10.1055/s-2005-921114.
   Google Scholar
• Lin T, Dollár P, Girshick RB, He K, Hariharan B, Belongie SJ. 2016. Feature pyramid networks for object detection. CoRR. abs/1612.03144. http://arxiv.org/abs/1612.03144.
   Google Scholar
• Lin T, Goyal P, Girshick RB, He K, Dollár P. 2017. Focal loss for dense object detection. CoRR. abs/1708.02002. http://arxiv.org/abs/1708.02002.
   Google Scholar
• Nair V, Hinton GE. 2010. Rectified linear units improve restricted Boltzmann machines. In: Icml.
   Google Scholar
• Patel DA, Lappas BM, Vaezi MF. 2017. An overview of achalasia and its subtypes. Gastroenterol Hepatol. 13(7):411.
   Google Scholar
• Peng C, Zhang X, Yu G, Luo G, Sun J. 2017. Large kernel matters–improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu (HI). p. 4353–4361.
   Google Scholar
• Playout C, Duval R, Cheriet F. 2018. A multitask learning architecture for simultaneous segmentation of bright and red lesions in fundus images. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Vol. 11071. Granada, Spain: Springer; pp. 101–108.
   Google Scholar
• Pohl D, Tutuian R. 2007. Achalasia: an overview of diagnosis and treatment. J Gastrointestinal Liver Dis. 16(3):297.
   Google Scholar
• Rakita S, Bloomston M, Villadolid D, Thometz D, Zervos E, Rosemurgy A. 2005. Esophagotomy during laparoscopic Heller myotomy cannot be predicted by preoperative therapies and does not influence long-term outcome. J Gastrointestinal Surg. 9(2):159–164. doi:10.1016/j.gassur.2004.10.015.
   Google Scholar
• Reynolds JC, Parkman HP. 1989. Achalasia. Gastroenterol Clin North Am. 18(2):223–255. doi:10.1016/S0889-8553(21)00676-2.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2015. MICCAI 2015 . Vol. 9351. Munich, Germany: Springer; pp. 234–241.
   Google Scholar
• Shiwaku H, Yamashita K, Ohmiya T, Nimura S, Shiwaku Y, Inoue H, Hasegawa S. 2018. New endoscopic finding of esophageal achalasia with ST hood short type: corona appearance. PLos one. 13(7):e0199955. doi:10.1371/journal.pone.0199955.
   Google Scholar
• Society JE. 2017. Descriptive rules for achalasia of the esophagus, June 2012. Esophagus. 14(4):275–289.
   Google Scholar
• Torres-Aguilera M, Troche JMR. 2018. Achalasia and esophageal cancer: risks and links. Clin Exp Gastroenterol. 11:309. doi:10.2147/CEG.S141642.
   Google Scholar
• Trockman A, Kolter JZ. 2022. Patches are all you need? arXiv preprint arXiv:220109792.
   Google Scholar
• Vaezi MF, Richter JE, American College of Gastroenterology Practice Parameter Committee. 1999. Diagnosis and management of achalasia. J Am Coll Gastroenterol. 94(12):3406–3412. doi:10.1111/j.1572-0241.1999.01639.x.
   Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. 2017. Attention is all you need. Adv Neural Inf Process Syst. 30:6000–6010.
   Google Scholar
• Woo S, Park J, Lee J, Kweon IS. 2018. CBAM: convolutional block attention module. CoRR. abs/1807.06521. doi:10.48550/arXiv.1807.06521
   Google Scholar
• Yu F, Koltun V. 2015. Multi-scale context aggregation by dilated convolutions. CoRR. abs/1511.07122 . .
   Google Scholar
• Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y. 2019. CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea. p. 6023–6032.
   Google Scholar
• Zhang H, Cisse M, Dauphin YN, Lopez-Paz D. 2017. Mixup: beyond empirical risk minimization. CoRR. abs/1710.09412.
   Google Scholar