Top Papers: KI in der Krebserkennung (2020)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2020 (von 7.310 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.

2018 2019 2020 2021

#	Paper	Zitationen
1	The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah et al. Radiology	3.694
2	International evaluation of an AI system for breast cancer screening Scott Mayer McKinney, Marcin Sieniek, Varun Godbole et al. Nature	2.994
3	Automated detection of COVID-19 cases using deep neural networks with X-ray images Tülin Öztürk, Muhammed Talo, Eylul Azra Yildirim et al. Computers in Biology and Medicine	2.659
4	Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Ioannis D. Apostolopoulos, Tzani A. Mpesiana Physical and Engineering Sciences in Medicine	2.381
5	CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images Asif Iqbal Khan, Junaid Latief Shah, Mohammad Mudasir Bhat Computer Methods and Programs in Biomedicine	1.333
6	Radiomics in medical imaging—“how-to” guide and critical reflection Janita E. van Timmeren, D. Cester, Stephanie Tanadini‐Lang et al. Insights into Imaging	1.216
7	Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers John Mongan, Linda Moy, Charles E. Kahn Radiology Artificial Intelligence	1.171
8	COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images Ezz El‐Din Hemdan, Marwa A. Shouman, Mohamed Esmail Karar arXiv (Cornell University)	972
9	Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation Nima Tajbakhsh, Laura Jeyaseelan, Qian Li et al. Medical Image Analysis	961
10	History of artificial intelligence in medicine Vivek Kaul, Sarah Enslin, Seth A. Gross Gastrointestinal Endoscopy	945
11	Human–computer collaboration for skin cancer recognition Philipp Tschandl, Claus Rinner, Zoé Apalla et al. Nature Medicine	861
12	COVID-19 Image Data Collection Joseph Cohen, Paul Morrison, Lan Dao arXiv (Cornell University)	818
13	The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions Thomas J. Littlejohns, Jo Holliday, Lorna M Gibson et al. Nature Communications	798
14	COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images Ferhat Uçar, Deniz Korkmaz Medical Hypotheses	796
15	CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection Abdul Waheed, Muskan Goyal, Deepak Gupta et al. IEEE Access	764
16	Detection of Coronavirus Disease (COVID-19) Based on Deep Features Prabira Kumar Sethy, Santi Kumari Behera Preprints.org	746
17	Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks Todd Hollon, Balaji Pandian, Arjun R. Adapa et al. Nature Medicine	741
18	A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images V. S. Chouhan, Sanjay Kumar Singh, Aditya Khamparia et al. Applied Sciences	728
19	Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label Chuansheng Zheng, Xianbo Deng, Qiang Fu et al. medRxiv	726
20	Deep Learning in Medical Image Analysis Heang‐Ping Chan, Ravi K. Samala, Lubomir M. Hadjiiski et al. Advances in experimental medicine and biology	705
21	Deep neural network models for computational histopathology: A survey Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel Medical Image Analysis	697
22	Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study Wouter Bulten, Hans Pinckaers, Hester van Boven et al. The Lancet Oncology	664
23	Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis Yu Fu, Alexander W. Jung, Ramón Viñas et al. Nature Cancer	661
24	A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT Xinggang Wang, Xianbo Deng, Qing Fu et al. IEEE Transactions on Medical Imaging	650
25	Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet Harsh Panwar, P. K. Gupta, Mohammad Khubeb Siddiqui et al. Chaos Solitons & Fractals	645
26	CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation Shuanglang Feng, Heming Zhao, Fei Shi et al. IEEE Transactions on Medical Imaging	644
27	AI in Medical Imaging Informatics: Current Challenges and Future Directions Andreas S. Panayides, Amir A. Amini, Nenad Filipović et al. IEEE Journal of Biomedical and Health Informatics	643
28	Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays Luca Brunese, Francesco Mercaldo, Alfonso Reginelli et al. Computer Methods and Programs in Biomedicine	628
29	Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis Agostina J. Larrazabal, Nicolás Nieto, Victoria Peterson et al. Proceedings of the National Academy of Sciences	626
30	Deep learning in cancer pathology: a new generation of clinical biomarkers Amelie Echle, Niklas Rindtorff, Titus J. Brinker et al. British Journal of Cancer	618
31	A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images Md. Zabirul Islam, Md. Milon Islam, Amanullah Asraf Informatics in Medicine Unlocked	616
32	Pan-cancer image-based detection of clinically actionable genetic alterations Jakob Nikolas Kather, Lara R. Heij, Heike I. Grabsch et al. Nature Cancer	613
33	Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning Aayush Jaiswal, Neha Gianchandani, Dilbag Singh et al. Journal of Biomolecular Structure and Dynamics	612
34	Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer Xueyi Zheng, Yao Zhao, Yini Huang et al. Nature Communications	608
35	Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study Peter Ström, Kimmo Kartasalo, Henrik Olsson et al. The Lancet Oncology	602
36	3D Deep Learning on Medical Images: A Review Satya P. Singh MDPI (MDPI AG)	600
37	Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial Alessandro Repici, Matteo Badalamenti, Roberta Maselli et al. Gastroenterology	586
38	Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment Hema Sekhar Reddy Rajula, Giuseppe Verlato, Mirko Manchia et al. Medicina	576
39	How Machine Learning Will Transform Biomedicine Jeremy Goecks, Vahid Jalili, Laura M. Heiser et al. Cell	573
40	Machine and deep learning methods for radiomics Michele Avanzo, Lise Wei, Joseph Stancanello et al. Medical Physics	567
41	A review of the application of deep learning in medical image classification and segmentation Lei Cai, Jingyang Gao, Di Zhao Annals of Translational Medicine	557
42	Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning Mohamed Loey, Florentín Smarandache, Nour Eldeen M. Khalifa Symmetry	551
43	Medical Image Segmentation based on U-Net: A Review Getao Du, Xu Cao, Jimin Liang et al. Journal of Imaging Science and Technology	540
44	Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis Richard J. Chen, Ming Y. Lu, Jingwen Wang et al. IEEE Transactions on Medical Imaging	532
45	Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation Xiaomeng Li, Lequan Yu, Hao Chen et al. IEEE Transactions on Neural Networks and Learning Systems	530
46	COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios Rodolfo M. Pereira, Diego Bertolini, Lucas Teixeira et al. Computer Methods and Programs in Biomedicine	528
47	Deep learning in medical image registration: a survey Grant Haskins, Uwe Kruger, Pingkun Yan Machine Vision and Applications	527
48	The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset Ibrahem Kandel, Mauro Castelli ICT Express	521
49	A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images Harsh Panwar, P. K. Gupta, Mohammad Khubeb Siddiqui et al. Chaos Solitons & Fractals	521
50	Deep learning based detection and analysis of COVID-19 on chest X-ray images Rachna Jain, Meenu Gupta, Soham Taneja et al. Applied Intelligence	517

Top Papers: KI in der Krebserkennung (2020)

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