Top Papers: KI in der Krebserkennung (2021)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2021 (von 9.122 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.

2019 2020 2021 2022

#	Paper	Zitationen
1	U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications Nahian Siddique, Sidike Paheding, Colin Elkin et al. IEEE Access	1.835
2	Deep learning-enabled medical computer vision Andre Esteva, Katherine Chou, Serena Yeung et al. npj Digital Medicine	1.219
3	TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation Yundong Zhang, Huiye Liu, Qiang Hu Lecture notes in computer science	1.114
4	Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images Tawsifur Rahman, Amith Khandakar, Yazan Qiblawey et al. Computers in Biology and Medicine	1.097
5	A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos et al. Proceedings of the IEEE	918
6	A review of medical image data augmentation techniques for deep learning applications Phillip Chlap, Hang Min, Nym Vandenberg et al. Journal of Medical Imaging and Radiation Oncology	915
7	Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning Noah F. Greenwald, Geneva Miller, Erick Moen et al. Nature Biotechnology	861
8	Deep learning in cancer diagnosis, prognosis and treatment selection Khoa Tran, Olga Kondrashova, Andrew P. Bradley et al. Genome Medicine	858
9	Convolutional neural networks in medical image understanding: a survey D. R. Sarvamangala, Raghavendra V. Kulkarni Evolutionary Intelligence	840
10	Deep learning in histopathology: the path to the clinic Jeroen van der Laak, Geert Litjens, Francesco Ciompi Nature Medicine	839
11	A Review of Deep-Learning-Based Medical Image Segmentation Methods Xiangbin Liu, Liping Song, Shuai Liu et al. Sustainability	835
12	Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images Ying Song, Shuangjia Zheng, Liang Li et al. IEEE/ACM Transactions on Computational Biology and Bioinformatics	834
13	Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis Ravi Aggarwal, Viknesh Sounderajah, Guy Martin et al. npj Digital Medicine	830
14	Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence Gary S. Collins, Paula Dhiman, Constanza L. Andaur Navarro et al. BMJ Open	757
15	Predicting cancer outcomes with radiomics and artificial intelligence in radiology Kaustav Bera, Nathaniel Braman, Amit Gupta et al. Nature Reviews Clinical Oncology	744
16	Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning Bin Li, Yin Li, Kevin W. Eliceiri —	742
17	Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM Parvathaneni Naga Srinivasu, Jalluri Gnana SivaSai, Muhammad Fazal Ijaz et al. Sensors	721
18	Artificial Intelligence in Cancer Research and Precision Medicine Bhavneet Bhinder, Coryandar Gilvary, Neel S. Madhukar et al. Cancer Discovery	715
19	Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond Guang Yang, Qinghao Ye, Jun Xia Information Fusion	680
20	AI applications to medical images: From machine learning to deep learning Isabella Castiglioni, Leonardo Rundo, Marina Codari et al. Physica Medica	678
21	Multimodal deep learning models for early detection of Alzheimer’s disease stage Janani Venugopalan, Tong Li, Hamid Reza Hassanzadeh et al. Scientific Reports	670
22	Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation Michael Yeung, Evis Sala, Carola‐Bibiane Schönlieb et al. Computerized Medical Imaging and Graphics	608
23	A review on deep learning in medical image analysis S. Suganyadevi, V. Seethalakshmi, K. Balasamy International Journal of Multimedia Information Retrieval	594
24	The Clinician and Dataset Shift in Artificial Intelligence Samuel G. Finlayson, Adarsh Subbaswamy, Karandeep Singh et al. New England Journal of Medicine	579
25	Big Self-Supervised Models Advance Medical Image Classification Shekoofeh Azizi, Basil Mustafa, Fiona Ryan et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)	579
26	Harnessing multimodal data integration to advance precision oncology Kevin Boehm, Pegah Khosravi, R. Vanguri et al. Nature reviews. Cancer	557
27	Digital pathology and artificial intelligence in translational medicine and clinical practice Vipul Baxi, Robin Edwards, Michael Montalto et al. Modern Pathology	534
28	FAT-Net: Feature adaptive transformers for automated skin lesion segmentation Huisi Wu, Shihuai Chen, Guilian Chen et al. Medical Image Analysis	532
29	Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer Devvi Sarwinda, Radifa Hilya Paradisa, Alhadi Bustamam et al. Procedia Computer Science	516
30	Artificial intelligence and computational pathology Miao Cui, David Y. Zhang Laboratory Investigation	504
31	TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification Zhuchen Shao, Hao Bian, Yang Chen et al. arXiv (Cornell University)	501
32	INet: Convolutional Networks for Biomedical Image Segmentation Weihao Weng, Xin Zhu IEEE Access	488
33	A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework Mehedi Masud, Niloy Sikder, Abdullah-Al Nahid et al. Sensors	441
34	An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier Saloni Kumari, Deepika Kumar, Mamta Mittal International Journal of Cognitive Computing in Engineering	428
35	Artificial intelligence and machine learning for medical imaging: A technology review Ana María Barragán Montero, Umair Javaid, Gilmer Valdés et al. Physica Medica	413
36	A deep look into radiomics Camilla Scapicchio, Michela Gabelloni, Andrea Barucci et al. La radiologia medica	401
37	A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique Abeer Saber, Mohamed Sakr, Osama M. Abo-Seida et al. IEEE Access	396
38	An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models Md Shahin Ali, Md Sipon Miah, Jahurul Haque et al. Machine Learning with Applications	396
39	Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives Hang Yu, Laurence T. Yang, Qingchen Zhang et al. Neurocomputing	382
40	Pneumonia detection in chest X-ray images using an ensemble of deep learning models Rohit Kundu, Ritacheta Das, Zong Woo Geem et al. PLoS ONE	377
41	Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach Sara Hosseinzadeh Kassania, Peyman Hosseinzadeh Kassanib, Michal J. Wesolowskic et al. Journal of Applied Biomedicine	372
42	Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis Mohammed Amine Naji, Sanaa El Filali, K. Aarika et al. Procedia Computer Science	369
43	Radiomics in Oncology: A Practical Guide Joshua Shur, Simon Doran, Santosh Kumar et al. Radiographics	368
44	Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review Jinshou Yang, Ruiyuan Xu, Chengcheng Wang et al. Cancer Communications	352
45	DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images Suriya Murugan, Chandran Venkatesan, M. G. Sumithra et al. IEEE Access	351
46	Transfer learning techniques for medical image analysis: A review Padmavathi Kora, Chui Ping Ooi, Oliver Faust et al. Journal of Applied Biomedicine	345
47	Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms Roxana Daneshjou, Mary P. Smith, Mary Sun et al. JAMA Dermatology	343
48	Artificial intelligence for clinical oncology Benjamin H. Kann, Ahmed Hosny, Hugo J.W.L. Aerts Cancer Cell	341
49	VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images Anjany Sekuboyina, Malek El Husseini, Amirhossein Bayat et al. Medical Image Analysis	335
50	Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future Muhammad Iqbal, Zeeshan Javed, Haleema Sadia et al. Cancer Cell International	334

Top Papers: KI in der Krebserkennung (2021)

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