Top Papers: KI in der Krebserkennung (2019)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2019 (von 5.615 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.

2017 2018 2019 2020

#	Paper	Zitationen
1	A survey on Image Data Augmentation for Deep Learning Connor Shorten, Taghi M. Khoshgoftaar Journal Of Big Data	11.808
2	ilastik: interactive machine learning for (bio)image analysis Stuart Berg, Dominik Kutra, Thorben Kroeger et al. Nature Methods	3.485
3	Clinical-grade computational pathology using weakly supervised deep learning on whole slide images Gabriele Campanella, Matthew G. Hanna, Luke Geneslaw et al. Nature Medicine	2.458
4	MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation Nabil Ibtehaz, M. Sohel Rahman Neural Networks	2.194
5	CE-Net: Context Encoder Network for 2D Medical Image Segmentation Zaiwang Gu, Jun Cheng, Huazhu Fu et al. IEEE Transactions on Medical Imaging	2.131
6	Dataset of breast ultrasound images Walid Al-Dhabyani, Mohammed Mohammed Mohammed Gomaa, Hussien Khaled et al. Data in Brief	1.926
7	Generative adversarial network in medical imaging: A review Yi Xin, Ekta Walia, Paul Babyn Medical Image Analysis	1.790
8	Artificial intelligence in cancer imaging: Clinical challenges and applications Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath et al. CA A Cancer Journal for Clinicians	1.782
9	A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis Xiaoxuan Liu, Livia Faes, Aditya U. Kale et al. The Lancet Digital Health	1.735
10	Causability and explainability of artificial intelligence in medicine Andreas Holzinger, Georg Langs, Helmut Denk et al. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery	1.603
11	Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He et al. Journal of Digital Imaging	1.576
12	Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology Kaustav Bera, Kurt A. Schalper, David L. Rimm et al. Nature Reviews Clinical Oncology	1.482
13	Machine learning in medicine: a practical introduction Jenni A. M. Sidey-Gibbons, Chris Sidey‐Gibbons BMC Medical Research Methodology	1.236
14	Deep learning for cellular image analysis Erick Moen, Dylan Bannon, Takamasa Kudo et al. Nature Methods	1.233
15	Digital pathology and artificial intelligence Muhammad Khalid Khan Niazi, Anil V. Parwani, Metin N. Gürcan The Lancet Oncology	1.138
16	Deep convolutional neural network based medical image classification for disease diagnosis Samir S. Yadav, Shivajirao M. Jadhav Journal Of Big Data	1.047
17	Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong et al. PLoS Medicine	1.006
18	Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) Noel Codella, Veronica Rotemberg, Philipp Tschandl et al. arXiv (Cornell University)	982
19	The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges Zhenyu Liu, Shuo Wang, Di Dong et al. Theranostics	964
20	Recurrent residual U-Net for medical image segmentation Md Zahangir Alom, Chris Yakopcic, Mahmudul Hasan et al. Journal of Medical Imaging	823
21	A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation Nabila Abraham, Naimul Khan —	800
22	Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study Pu Wang, Tyler M. Berzin, Jeremy R. Glissen Brown et al. Gut	798
23	Deep Learning to Improve Breast Cancer Detection on Screening Mammography Li Shen, Laurie R. Margolies, Joseph H. Rothstein et al. Scientific Reports	796
24	A novel deep learning based framework for the detection and classification of breast cancer using transfer learning SanaUllah Khan, Naveed Islam, Zahoor Jan et al. Pattern Recognition Letters	773
25	The impact of artificial intelligence in medicine on the future role of the physician Abhimanyu S. Ahuja PeerJ	770
26	Deep Learning in Medical Ultrasound Analysis: A Review Shengfeng Liu, Yi Wang, Xin Yang et al. Engineering	759
27	Introduction to artificial intelligence in medicine Yoav Mintz, Ronit Brodie Minimally Invasive Therapy & Allied Technologies	740
28	Multi-Classification of Brain Tumor Images Using Deep Neural Network Hossam H. Sultan, Nancy M. Salem, Walid Al‐Atabany IEEE Access	717
29	A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction Adam Yala, Constance D. Lehman, Tal Schuster et al. Radiology	699
30	Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks Veit Sandfort, Ke Yan, Perry J. Pickhardt et al. Scientific Reports	664
31	Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening Nan Wu, Jason Phang, Jungkyu Park et al. IEEE Transactions on Medical Imaging	655
32	BACH: Grand challenge on breast cancer histology images Guilherme Aresta, Teresa Araújo, Scotty Kwok et al. Medical Image Analysis	649
33	Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges Shigao Huang, Jie Yang, Simon Fong et al. Cancer Letters	645
34	Transfusion: Understanding Transfer Learning for Medical Imaging Maithra Raghu, Chiyuan Zhang, Jon Kleinberg et al. arXiv (Cornell University)	642
35	An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare Okeke Stephen, Mangal Sain, Uchenna Joseph Maduh et al. Journal of Healthcare Engineering	606
36	Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging Yiwen Xu, Ahmed Hosny, Roman Zeleznik et al. Clinical Cancer Research	604
37	Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task Titus J. Brinker, Achim Hekler, Alexander Enk et al. European Journal of Cancer	555
38	Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology David Tellez, Geert Litjens, Péter Bándi et al. Medical Image Analysis	550
39	Attention Residual Learning for Skin Lesion Classification Jianpeng Zhang, Yutong Xie, Yong Xia et al. IEEE Transactions on Medical Imaging	548
40	A Multi-Organ Nucleus Segmentation Challenge Neeraj Kumar, Ruchika Verma, Deepak Anand et al. IEEE Transactions on Medical Imaging	517
41	Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study Philipp Tschandl, Noel Codella, Bengü Nisa Akay et al. The Lancet Oncology	514
42	How to Read Articles That Use Machine Learning Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause et al. JAMA	505
43	Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide Shelly Soffer, Avi Ben-Cohen, Orit Shimon et al. Radiology	501
44	Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network Fangzhou Liao, Ming Liang, Zhe Li et al. IEEE Transactions on Neural Networks and Learning Systems	495
45	A new era: artificial intelligence and machine learning in prostate cancer S. Larry Goldenberg, Guy Nir, Septimiu E. Salcudean Nature Reviews Urology	494
46	Artificial intelligence and machine learning in clinical development: a translational perspective Pratik Shah, Francis Kendall, Sean Khozin et al. npj Digital Medicine	489
47	Breast cancer detection using deep convolutional neural networks and support vector machines Dina A. Ragab, Maha Sharkas, Stephen Marshall et al. PeerJ	489
48	NAS-Unet: Neural Architecture Search for Medical Image Segmentation Yu Weng, Tianbao Zhou, Yujie Li et al. IEEE Access	486
49	Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer Kunal Nagpal, Davis Foote, Yun Liu et al. npj Digital Medicine	483
50	Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers Dong Wook Kim, Hye Young Jang, Kyung Won Kim et al. Korean Journal of Radiology	454

Top Papers: KI in der Krebserkennung (2019)

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