Top Papers: KI in der Krebserkennung (2018)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2018 (von 4.331 insgesamt).

Krebs frühzeitig zu erkennen kann Leben retten – und genau hier setzt KI an. Deep-Learning-Modelle erreichen inzwischen bei bestimmten Tumorarten eine Erkennungsgenauigkeit, die mit der erfahrener Pathologen vergleichbar ist. Die Forschung umfasst Hautkrebs-Screening, Brustkrebs-Mammographie, Lungennoduli-Erkennung und vieles mehr. Hier finden Sie die einflussreichsten und neuesten Studien zu diesem Thema.

2016 2017 2018 2019

#	Paper	Zitationen
1	Convolutional neural networks: an overview and application in radiology Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian et al. Insights into Imaging	4.458
2	Artificial intelligence in radiology Ahmed Hosny, Chintan Parmar, John Quackenbush et al. Nature reviews. Cancer	3.493
3	The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions Philipp Tschandl, Cliff Rosendahl, Harald Kittler Scientific Data	2.952
4	Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning Nicolas Coudray, Paolo Ocampo, Theodore Sakellaropoulos et al. Nature Medicine	2.776
5	H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes Xiaomeng Li, Hao Chen, Xiaojuan Qi et al. IEEE Transactions on Medical Imaging	2.393
6	Opportunities and obstacles for deep learning in biology and medicine Travers Ching, Daniel Himmelstein, Brett K. Beaulieu‐Jones et al. Journal of The Royal Society Interface	2.168
7	The practical implementation of artificial intelligence technologies in medicine Jianxing He, Sally L. Baxter, Jie Xu et al. Nature Medicine	2.108
8	U-Net: deep learning for cell counting, detection, and morphometry Thorsten Falk, Dominic Mai, Robert Bensch et al. Nature Methods	1.966
9	GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification Maayan Frid-Adar, Idit Diamant, Eyal Klang et al. Neurocomputing	1.852
10	Data augmentation for improving deep learning in image classification problem Agnieszka Mikołajczyk, Michał Grochowski —	1.655
11	Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists Holger A. Haenssle, Christine Fink, Roland Schneiderbauer et al. Annals of Oncology	1.505
12	Cell Detection with Star-Convex Polygons Uwe Schmidt, Martin Weigert, Coleman Broaddus et al. Lecture notes in computer science	1.443
13	Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study John R. Zech, Marcus A. Badgeley, Manway Liu et al. PLoS Medicine	1.394
14	LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity Christophe Nioche, Fanny Orlhac, Sarah Boughdad et al. Cancer Research	1.116
15	Predicting cancer outcomes from histology and genomics using convolutional networks Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad et al. Proceedings of the National Academy of Sciences	1.033
16	Radiomics: the facts and the challenges of image analysis Stefania Rizzo, Francesca Botta, Sara Raimondi et al. European Radiology Experimental	1.027
17	Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net) Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha et al. —	950
18	Machine Learning Methods for Histopathological Image Analysis Daisuke Komura, Shumpei Ishikawa Computational and Structural Biotechnology Journal	884
19	<scp>eD</scp>octor: machine learning and the future of medicine Guy Handelman, Hong Kuan Kok, Ronil V. Chandra et al. Journal of Internal Medicine	883
20	Variational image compression with a scale hyperprior Johannes Ballé, David Minnen, Saurabh Singh et al. arXiv (Cornell University)	859
21	The Morphological Approach to Segmentation: The Watershed Transformation Serge Beucher, Fernand Meyer —	847
22	Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction Seong Ho Park, Kyunghwa Han Radiology	821
23	Deep learning in medical imaging and radiation therapy Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski et al. Medical Physics	724
24	Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy Gregor Urban, Priyam Tripathi, Talal Alkayali et al. Gastroenterology	710
25	Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm Seung Seog Han, Myoung Shin Kim, Woohyung Lim et al. Journal of Investigative Dermatology	699
26	Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net)\n for Medical Image Segmentation Md Zahangir Alom, Md. Mahmudul Hasan, Chris Yakopcic et al. arXiv (Cornell University)	681
27	Attention-based Deep Multiple Instance Learning Maximilian Ilse, Jakub M. Tomczak, Max Welling arXiv (Cornell University)	671
28	Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map Peter Naylor, Marick Laé, Fabien Reyal et al. IEEE Transactions on Medical Imaging	668
29	Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists Alejandro Rodríguez‐Ruiz, Kristina Lång, Albert Gubern‐Mérida et al. JNCI Journal of the National Cancer Institute	649
30	Deep learning based tissue analysis predicts outcome in colorectal cancer Dmitrii Bychkov, Nina Linder, Riku Turkki et al. Scientific Reports	643
31	Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network Yuexiang Li, Linlin Shen Sensors	616
32	Deep Learning and Its Applications in Biomedicine Chensi Cao, Feng Liu, Hai Tan et al. Genomics Proteomics & Bioinformatics	614
33	Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks Hoo-Chang Shin, Neil Tenenholtz, Jameson K. Rogers et al. Lecture notes in computer science	612
34	Machine Learning in Medical Imaging Maryellen L. Giger Journal of the American College of Radiology	604
35	Deep learning in biomedicine Michael Wainberg, Daniele Merico, Andrew Delong et al. Nature Biotechnology	589
36	Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System Alejandro Rodríguez‐Ruiz, Elizabeth A. Krupinski, Jan-Jurre Mordang et al. Radiology	585
37	DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning Ke Yan, Xiaosong Wang, Le Lü et al. Journal of Medical Imaging	553
38	NiftyNet: a deep-learning platform for medical imaging Eli Gibson, Wenqi Li, Carole H. Sudre et al. Computer Methods and Programs in Biomedicine	545
39	Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine Nancy A. Obuchowski, Jennifer Bullen Physics in Medicine and Biology	525
40	GAIN: Missing Data Imputation using Generative Adversarial Nets Jinsung Yoon, James Jordon, Mihaela van der Schaar arXiv (Cornell University)	525
41	Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha et al. Journal of Magnetic Resonance Imaging	523
42	Artificial Intelligence and Digital Pathology: Challenges and Opportunities Hamid R. Tizhoosh, Liron Pantanowitz Journal of Pathology Informatics	522
43	Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets Jeremy Kawahara, Sara Daneshvar, Giuseppe Argenziano et al. IEEE Journal of Biomedical and Health Informatics	513
44	Deep learning for image-based cancer detection and diagnosis − A survey Zilong Hu, Jinshan Tang, Ziming Wang et al. Pattern Recognition	501
45	Application of deep transfer learning for automated brain abnormality classification using MR images Muhammed Talo, Ulaş Baran Baloğlu, Özal Yıldırım et al. Cognitive Systems Research	500
46	Deep Learning and Medical Diagnosis: A Review of Literature Mihalj Bakator, Dragica Radosav Multimodal Technologies and Interaction	491
47	Deep Learning in Radiology Morgan P. McBee, Omer A. Awan, Andrew Colucci et al. Academic Radiology	485
48	Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm Jae‐Hong Lee, Do‐Hyung Kim, Seong‐Nyum Jeong et al. Journal of Periodontal & Implant Science	482
49	Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review Titus J. Brinker, Achim Hekler, Jochen Utikal et al. Journal of Medical Internet Research	473
50	Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer David F. Steiner, Robert MacDonald, Yun Liu et al. The American Journal of Surgical Pathology	467

Top Papers: KI in der Krebserkennung (2018)

Verwandte Seiten