Diagnostic ability of deep learning in detection of pancreatic tumour

[1]  Lirong Yin,et al.  Identifying Malignant Breast Ultrasound Images Using ViT-Patch , 2023, Applied Sciences.

[2]  Y. Li,et al.  How Effective are Metal Nanotherapeutic Platforms Against Bacterial Infections? A Comprehensive Review of Literature , 2023, International journal of nanomedicine.

[3]  Z. Dai,et al.  Subsurface sedimentary structure identification using deep learning: A review , 2023, Earth-Science Reviews.

[4]  A. Bergamaschi,et al.  Epigenomic Blood-based Early Detection of Pancreatic Cancer Employing cell free DNA. , 2023, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[5]  Kuai He,et al.  Coupling in-situ synchrotron X-ray radiography and FT-IR spectroscopy reveal thermally-induced subsurface microstructure evolution of solid propellants , 2023, Combustion and Flame.

[6]  Hongyu Zhang,et al.  Surface-functionalized design of blood-contacting biomaterials for preventing coagulation and promoting hemostasis , 2023, Friction.

[7]  R. Brand,et al.  Pancreatic Cancer: Changing Epidemiology and New Approaches to Risk assessment, Early Detection, and Prevention. , 2023, Gastroenterology.

[8]  Yiqi Chen,et al.  EXCESSIVE GLIOSIS AFTER VITRECTOMY FOR THE HIGHLY MYOPIC MACULAR HOLE , 2023, Retina.

[9]  Qixiong Zhang,et al.  A brief review of polysialic acid-based drug delivery systems. , 2023, International journal of biological macromolecules.

[10]  Jun Xia,et al.  Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network , 2023, Cancer research.

[11]  Kai Jin,et al.  iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis , 2023, Journal of clinical medicine.

[12]  Xiaoliang Xie,et al.  Deduction of sudden rainstorm scenarios: integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory , 2022, Natural Hazards.

[13]  N. Tatonetti,et al.  Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer , 2022, Patterns.

[14]  Sukki Cho,et al.  Serum protein profiling of lung, pancreatic, and colorectal cancers reveals alcohol consumption-mediated disruptions in early-stage cancer detection , 2022, Heliyon.

[15]  Kai Jin,et al.  Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning , 2022, British Journal of Ophthalmology.

[16]  Lirong Yin,et al.  2D/3D Multimode Medical Image Alignment Based on Spatial Histograms , 2022, Applied Sciences.

[17]  Lu Lu,et al.  New insights into natural products that target the gut microbiota: Effects on the prevention and treatment of colorectal cancer , 2022, Frontiers in Pharmacology.

[18]  Shuai Chen,et al.  An Effective WSSENet-Based Similarity Retrieval Method of Large Lung CT Image Databases , 2022, KSII Trans. Internet Inf. Syst..

[19]  Xiaomei Qin,et al.  Improved Image Fusion Method Based on Sparse Decomposition , 2022, Electronics.

[20]  Shuaijian Ni,et al.  Targeting loop3 of sclerostin preserves its cardiovascular protective action and promotes bone formation , 2022, Nature Communications.

[21]  Shuaijian Ni,et al.  Therapeutic aptamer targeting sclerostin loop3 for promoting bone formation without increasing cardiovascular risk in osteogenesis imperfecta mice , 2022, Theranostics.

[22]  Yi Zhuang,et al.  Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks , 2022, Wireless Communications and Mobile Computing.

[23]  Yun-xia Wu,et al.  Visualization of Zika Virus Infection via a Light-Initiated Bio-Orthogonal Cycloaddition Labeling Strategy , 2022, Frontiers in Bioengineering and Biotechnology.

[24]  N. Tatonetti,et al.  Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer , 2022, J. Biomed. Informatics.

[25]  Yogesh Kumar,et al.  Pancreatic Cancer Detection using Machine and Deep Learning Techniques , 2022, 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM).

[26]  Feiyang Zhang,et al.  Unraveling of Advances in 3D-Printed Polymer-Based Bone Scaffolds , 2022, Polymers.

[27]  Maha M. Althobaiti,et al.  Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans , 2022, Journal of healthcare engineering.

[28]  D. Akin,et al.  Advanced Point‐of‐Care Testing Technologies for Human Acute Respiratory Virus Detection , 2021, Advanced materials.

[29]  Hao Geng,et al.  Homocysteine inhibits pro-insulin receptor cleavage and causes insulin resistance via protein cysteine-homocysteinylation. , 2021, Cell reports.

[30]  Yunlun Li,et al.  Long noncoding RNA p21 enhances autophagy to alleviate endothelial progenitor cells damage and promote endothelial repair in hypertension through SESN2/AMPK/TSC2 pathway. , 2021, Pharmacological research.

[31]  C Malathy,et al.  Deep Transfer Learning for Pancreatic Cancer Detection , 2021, 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT).

[32]  F. Hu,et al.  Is Health Contagious?—Based on Empirical Evidence From China Family Panel Studies' Data , 2021, Frontiers in Public Health.

[33]  Hui Chen,et al.  Regulatory mechanisms of lipid biosynthesis in microalgae , 2021, Biological reviews of the Cambridge Philosophical Society.

[34]  Tao Yu,et al.  Atractylenolide I enhances responsiveness to immune checkpoint blockade therapy by activating tumor antigen presentation. , 2021, The Journal of clinical investigation.

[35]  L. Huo,et al.  Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics , 2021, EJNMMI Research.

[36]  Hiroyuki Sugimori,et al.  Improvement in the Convolutional Neural Network for Computed Tomography Images , 2021, Applied Sciences.

[37]  N. Malats,et al.  A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case–control study , 2020, PLoS medicine.

[38]  Cheng-Fu Xu,et al.  Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis , 2020, World journal of gastroenterology.

[39]  Maili Liu,et al.  Hyperpolarized Xe NMR signal advancement by metal-organic framework entrapment in aqueous solution , 2020, Proceedings of the National Academy of Sciences.

[40]  Yuhsiang M. Tsai,et al.  Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. , 2020, The Lancet. Digital health.

[41]  J. Manson,et al.  Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population , 2020, Cancer Epidemiology, Biomarkers & Prevention.

[42]  W. Tompson,et al.  Problems and Prospects: , 2020, Jewish Christianity.

[43]  A. Yuille,et al.  Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble , 2020, MICCAI.

[44]  S. Batra,et al.  Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning , 2020, Clinical Cancer Research.

[45]  David S. Melnick,et al.  International evaluation of an AI system for breast cancer screening , 2020, Nature.

[46]  R. Braren,et al.  A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging , 2019, European Radiology Experimental.

[47]  Minhu Chen,et al.  Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network , 2019, Neuroendocrinology.

[48]  Yan Wang,et al.  Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience. , 2019, Journal of the American College of Radiology : JACR.

[49]  Y. Fujisawa,et al.  Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis , 2018, The British journal of dermatology.

[50]  Alan L. Yuille,et al.  Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma , 2018, MICCAI.

[51]  L. Qiu,et al.  A preliminary study , 2018, Medicine.

[52]  Wei Wang,et al.  Deep learning aided decision support for pulmonary nodules diagnosing: a review. , 2018, Journal of thoracic disease.

[53]  Xinqi Gong,et al.  Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images , 2018, BMC Syst. Biol..

[54]  O. Abe,et al.  Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study. , 2017, Radiology.

[55]  Bin Zhou,et al.  Early detection of pancreatic cancer: Where are we now and where are we going? , 2017, International journal of cancer.

[56]  J. Berlin,et al.  Potentially Curable Pancreatic Cancer: American Society of Clinical Oncology Clinical Practice Guideline Update. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[57]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[58]  Bram van Ginneken,et al.  Towards automatic pulmonary nodule management in lung cancer screening with deep learning , 2016, Scientific Reports.

[59]  A. Khorana,et al.  Locally Advanced, Unresectable Pancreatic Cancer: American Society of Clinical Oncology Clinical Practice Guideline. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[60]  Zhuiyang Zhang,et al.  Presurgical Evaluation of Pancreatic Cancer: A Comprehensive Imaging Comparison of CT Versus MRI. , 2016, AJR. American journal of roentgenology.

[61]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[63]  E. Fishman,et al.  Recent progress in pancreatic cancer , 2013, CA: a cancer journal for clinicians.

[64]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[65]  S. Chari.  Detecting early pancreatic cancer: problems and prospects. , 2007, Seminars in oncology.

[66]  D. Lobo,et al.  Recent developments in diagnosis of pancreatic cancer , 2004, BMJ : British Medical Journal.

[67]  M. Burns,et al.  Case-Control Study , 2020, Definitions.

[68]  R. Jeffrey,et al.  MDCT in Pancreatic adenocarcinoma: prediction of vascular invasion and resectability using a multiphasic technique with curved planar reformations. , 2004, AJR. American journal of roentgenology.

[69]  H. Soeda,et al.  New Classification of Small Pulmonary Nodules by Margin Characteristics on Highresolution CT , 1999, Acta radiologica.

[70]  B. Karlan.  Where are we now and where are we going? , 1995, Gynecologic oncology.

[71]  Bence Kato,et al.  Phased array guided wave propagation in curved plates , 2023, Mechanical Systems and Signal Processing.

[72]  H. Kocher,et al.  Pancreatic Cancer , 2019, Methods in Molecular Biology.

[73]  S. Temin,et al.  Systemic Therapy for Stage IV Non-Small-Cell Lung Cancer: American Society of Clinical Oncology Clinical Practice Guideline Update. , 2016, Journal of oncology practice / American Society of Clinical Oncology.

[74]  Elliot K Fishman,et al.  Pancreatic ductal adenocarcinoma radiology reporting template: consensus statement of the Society of Abdominal Radiology and the American Pancreatic Association. , 2014, Radiology.

[75]  古谷 清美,et al.  New classification of small pulmonary nodules by margin characteristics on high-resolution CT , 2002 .

[76]  Robert C. Wolpert,et al.  A Review of the , 1985 .