Top Papers: KI in der Krebserkennung (2022)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2022 (von 10.111 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.

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#	Paper	Zitationen
1	Explainable artificial intelligence (XAI) in deep learning-based medical image analysis Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs et al. Medical Image Analysis	1.103
2	The Liver Tumor Segmentation Benchmark (LiTS) Patrick Bilic, Patrick Ferdinand Christ, Hongwei Li et al. Medical Image Analysis	1.096
3	Recent advances and clinical applications of deep learning in medical image analysis Xuxin Chen, Ximin Wang, Ke Zhang et al. Medical Image Analysis	952
4	Transfer learning for medical image classification: a literature review Kim Eun Hee, Alejandro Cosa‐Linan, Nandhini Santhanam et al. BMC Medical Imaging	858
5	DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation Ailiang Lin, Bingzhi Chen, Jiayu Xu et al. IEEE Transactions on Instrumentation and Measurement	814
6	UNeXt: MLP-Based Rapid Medical Image Segmentation Network Jeya Maria Jose Valanarasu, Vishal M. Patel Lecture notes in computer science	746
7	Medical image segmentation using deep learning: A survey Risheng Wang, Tao Lei, Ruixia Cui et al. IET Image Processing	742
8	Artificial intelligence for multimodal data integration in oncology Jana Lipková, Richard J. Chen, Bowen Chen et al. Cancer Cell	617
9	Transformer-based unsupervised contrastive learning for histopathological image classification Xiyue Wang, Sen Yang, Jun Zhang et al. Medical Image Analysis	591
10	Self-supervised learning in medicine and healthcare Rayan Krishnan, Pranav Rajpurkar, Eric J. Topol Nature Biomedical Engineering	557
11	Machine learning for medical imaging: methodological failures and recommendations for the future Gaël Varoquaux, Veronika Cheplygina npj Digital Medicine	555
12	Towards a guideline for evaluation metrics in medical image segmentation Dominik Müller, Iñaki Soto‐Rey, Frank Krämer BMC Research Notes	533
13	Pan-cancer integrative histology-genomic analysis via multimodal deep learning Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson et al. Cancer Cell	510
14	Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning Richard J. Chen, Chengkuan Chen, Yicong Li et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)	480
15	Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen et al. Nature Medicine	456
16	Transformers in medical image analysis Kelei He, Gan Chen, Zhuoyuan Li et al. Intelligent Medicine	422
17	The future of early cancer detection Rebecca C. Fitzgerald, Antonis C. Antoniou, Ljiljana Fruk et al. Nature Medicine	416
18	Artificial intelligence in histopathology: enhancing cancer research and clinical oncology Artem Shmatko, Narmin Ghaffari Laleh, Moritz Gerstung et al. Nature Cancer	395
19	Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI) Ritse M. Mann, Alexandra Athanasiou, Pascal Baltzer et al. European Radiology	387
20	Generative Adversarial Networks in Medical Image augmentation: A review Yizhou Chen, Xu-Hua Yang, Zihan Wei et al. Computers in Biology and Medicine	387
21	DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification Hongrun Zhang, Yanda Meng, Yitian Zhao et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)	386
22	Data augmentation for medical imaging: A systematic literature review Fabio Garcea, Alessio Serra, Fabrizio Lamberti et al. Computers in Biology and Medicine	366
23	Mixed Transformer U-Net for Medical Image Segmentation Hongyi Wang, Shiao Xie, Lanfen Lin et al. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)	343
24	Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning Walaa Gouda, Najm Us Sama, Ghada Al-Waakid et al. Healthcare	337
25	Breast cancer in India: Present scenario and the challenges ahead Ravi Mehrotra, Kavita Yadav World Journal of Clinical Oncology	336
26	Breast cancer detection using deep learning: Datasets, methods, and challenges ahead Nusrat Mohi Ud Din, Rayees Ahmad Dar, Muzafar Rasool et al. Computers in Biology and Medicine	329
27	Machine learning and deep learning approach for medical image analysis: diagnosis to detection Meghavi Rana, Megha Bhushan Multimedia Tools and Applications	328
28	Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer Kevin Boehm, Emily A. Aherne, Lora H. Ellenson et al. Nature Cancer	322
29	AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images Gongping Chen, Lei Li, Yu Dai et al. IEEE Transactions on Medical Imaging	313
30	The Role of Artificial Intelligence in Early Cancer Diagnosis Benjamin Hunter, Sumeet Hindocha, Richard W. Lee Cancers	313
31	U-Net-Based Medical Image Segmentation Xiaoxia Yin, Le Sun, Yuhan Fu et al. Journal of Healthcare Engineering	310
32	Breast cancer detection using artificial intelligence techniques: A systematic literature review Ali Bou Nassif, Manar Abu Talib, Qassim Nasir et al. Artificial Intelligence in Medicine	305
33	ConvUNeXt: An efficient convolution neural network for medical image segmentation Zhimeng Han, Muwei Jian, Gai‐Ge Wang Knowledge-Based Systems	304
34	Federated learning and differential privacy for medical image analysis Mohammed Adnan, Shivam Kalra, Jesse C. Cresswell et al. Scientific Reports	301
35	Artificial intelligence and machine learning in cancer imaging Dow‐Mu Koh, Nickolas Papanikolaou, Ulrich Bick et al. Communications Medicine	290
36	A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images Zhiming Cui, Yu Fang, Lanzhuju Mei et al. Nature Communications	287
37	Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin et al. Expert Systems with Applications	284
38	Literature review: efficient deep neural networks techniques for medical image analysis Mohamed A. Abdou Neural Computing and Applications	279
39	Brain Imaging Generation with Latent Diffusion Models Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon et al. Lecture notes in computer science	279
40	Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion Kiran Jabeen, Muhammad Attique Khan, Majed Alhaisoni et al. Sensors	276
41	Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation Andre M. Carrington, Douglas G. Manuel, Paul W. Fieguth et al. IEEE Transactions on Pattern Analysis and Machine Intelligence	275
42	Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends Imran Qureshi, Junhua Yan, Qaisar Abbas et al. Information Fusion	269
43	Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing Shahid Mehmood, Taher M. Ghazal, Muhammad Adnan Khan et al. IEEE Access	268
44	A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches Xintong Li, Chen Li, Md Mamunur Rahaman et al. Artificial Intelligence Review	256
45	Deep Neural Networks for Medical Image Segmentation Priyanka Malhotra, Sheifali Gupta, Deepika Koundal et al. Journal of Healthcare Engineering	249
46	Synthetic data generation for tabular health records: A systematic review Mikel Hernandez, Gorka Epelde, Ane Alberdi et al. Neurocomputing	248
47	Hybrid convolutional neural networks with SVM classifier for classification of skin cancer Duggani Keerthana, Vipin Venugopal, Malaya Kumar Nath et al. Biomedical Engineering Advances	248
48	Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review O. T. G. Jones, Rubeta Matin, Mihaela van der Schaar et al. The Lancet Digital Health	247
49	Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques Umesh Kumar Lilhore, M. Poongodi, Amandeep Kaur et al. Computational and Mathematical Methods in Medicine	245
50	Medical deep learning—A systematic meta-review Jan Egger, Christina Gsaxner, Antonio Pepe et al. Computer Methods and Programs in Biomedicine	243

Top Papers: KI in der Krebserkennung (2022)

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