Top Papers: KI in der Krebserkennung (2024)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2024 (von 11.096 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.

2022 2023 2024 2025

#	Paper	Zitationen
1	Segment anything in medical images Jun Ma, Yuting He, Feifei Li et al. Nature Communications	2.069
2	Towards a general-purpose foundation model for computational pathology Richard J. Chen, Tong Ding, Ming Y. Lu et al. Nature Medicine	923
3	Evaluation metrics and statistical tests for machine learning Oona Rainio, Jarmo Teuho, Riku Klén Scientific Reports	867
4	A visual-language foundation model for computational pathology Ming Y. Lu, Bowen Chen, Drew F. K. Williamson et al. Nature Medicine	570
5	A whole-slide foundation model for digital pathology from real-world data Hanwen Xu, Naoto Usuyama, Jaspreet Bagga et al. Nature	516
6	ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation Ming Kang, Chee‐Ming Ting, Fung Fung Ting et al. Image and Vision Computing	394
7	Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images Satvik Vats, Vikrant Sharma, Amit Singh et al. Expert Systems with Applications	386
8	AI in diagnostic imaging: Revolutionising accuracy and efficiency Mohamed Khalifa, Mona Albadawy Computer Methods and Programs in Biomedicine Update	365
9	A pathology foundation model for cancer diagnosis and prognosis prediction Xiyue Wang, Junhan Zhao, Eliana Marostica et al. Nature	345
10	Towards Generalist Biomedical AI Tao Tu, Shekoofeh Azizi, Danny Driess et al. NEJM AI	326
11	Federated learning for medical image analysis: A survey Hao Guan, Pew‐Thian Yap, Andrea Bozoki et al. Pattern Recognition	325
12	EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation Md Mostafijur Rahman, Mustafa Munir, Diana Marculescu —	317
13	A multimodal generative AI copilot for human pathology Ming Y. Lu, Bowen Chen, Drew F. K. Williamson et al. Nature	308
14	A foundation model for clinical-grade computational pathology and rare cancers detection Eugene Vorontsov, Alican Bozkurt, Adam Casson et al. Nature Medicine	303
15	Channel prior convolutional attention for medical image segmentation Hejun Huang, Zuguo Chen, Ying Zou et al. Computers in Biology and Medicine	265
16	Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update Ali S. Tejani, Michail E. Klontzas, Anthony A. Gatti et al. Radiology Artificial Intelligence	263
17	Segment anything model for medical image segmentation: Current applications and future directions Yichi Zhang, Zhenrong Shen, Rushi Jiao Computers in Biology and Medicine	247
18	Developing clinical prediction models: a step-by-step guide Orestis Efthimiou, Michael Seo, Konstantina Chalkou et al. BMJ	242
19	CellViT: Vision Transformers for precise cell segmentation and classification Fabian Hörst, Moritz Rempe, Lukas Heine et al. Medical Image Analysis	241
20	Using RNN Artificial Neural Network to Predict the Occurrence of Gastric Cancer in the Future of the World Seyed Masoud Ghoreishi Mokri, Newsha Valadbeygi, Khafaji Mohammed Balyasimovich International Journal of Innovative Science and Research Technology (IJISRT)	235
21	Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology – a recent scoping review Ehsan Ullah, Anil V. Parwani, Mirza Mansoor Baig et al. Diagnostic Pathology	230
22	U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation Jun Ma, Li Fei-Fei, Bo Wang arXiv (Cornell University)	212
23	MedSegDiff-V2: Diffusion-Based Medical Image Segmentation with Transformer Junde Wu, Wei Ji, Huazhu Fu et al. Proceedings of the AAAI Conference on Artificial Intelligence	212
24	Deep learning based multimodal biomedical data fusion: An overview and comparative review Junwei Duan, Jiaqi Xiong, Yinghui Li et al. Information Fusion	201
25	Osteoporosis Prediction Using VGG16 and ResNet50 Ashadu Jaman Shawon, Ibrahim Ibne Mostafa Gazi, Humaira Rashid Hiya et al. International Journal of Innovative Science and Research Technology (IJISRT)	191
26	Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms G. Mostafa, Hamdi A. Mahmoud, Tarek Abd El‐Hafeez et al. Journal Of Big Data	188
27	Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches Yan Xu, Rixiang Quan, Weiting Xu et al. Bioengineering	187
28	Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy Clare McGenity, Emily L. Clarke, Charlotte Jennings et al. npj Digital Medicine	175
29	A guide to artificial intelligence for cancer researchers Raquel Pérez-López, Narmin Ghaffari Laleh, Faisal Mahmood et al. Nature reviews. Cancer	165
30	Automatic machine learning model for enhanced partition and identification of breast disorders in breast MRI scan Harendra Singh, Arun Kumar Rana, Jayant Giri et al. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization	158
31	An Analysis of Algorithms and Methods Based on Image Processing for Medical Applications Aakifa Shahul, Balakumar Muniandi, Mukundan Appadurai Paramashivan et al. Advances in civil and industrial engineering book series	156
32	Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions Mohamed Khalifa, Mona Albadawy Computer Methods and Programs in Biomedicine Update	152
33	Foundation model for cancer imaging biomarkers Suraj Pai, Dennis Bontempi, Ibrahim Hadžić et al. Nature Machine Intelligence	150
34	A review of deep learning-based information fusion techniques for multimodal medical image classification Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze et al. Computers in Biology and Medicine	150
35	A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs Sheng Zhang, Yanbo Xu, Naoto Usuyama et al. NEJM AI	144
36	From CNN to Transformer: A Review of Medical Image Segmentation Models Wenjian Yao, Jiajun Bai, Wei Liao et al. Journal of Imaging Informatics in Medicine	140
37	Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions William Lotter, Michael J. Hassett, Nikolaus Schultz et al. Cancer Discovery	135
38	Heterogeneity and predictors of the effects of AI assistance on radiologists Feiyang Yu, Alex Moehring, Oishi Banerjee et al. Nature Medicine	135
39	Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture Zhen Ling Teo, Liyuan Jin, Nan Liu et al. Cell Reports Medicine	132
40	Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4 Juexiao Zhou, Xiaonan He, Liyuan Sun et al. Nature Communications	130
41	PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge Chih-Hsuan Wei, Alexis Allot, Po‐Ting Lai et al. Nucleic Acids Research	127
42	Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma Tirtha Chanda, Katja Hauser, Sarah Hobelsberger et al. Nature Communications	124
43	MaxCerVixT: A novel lightweight vision transformer-based Approach for precise cervical cancer detection İshak Paçal Knowledge-Based Systems	123
44	A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions Tauhidul Islam, Md. Sadman Hafiz, Jamin Rahman Jim et al. Healthcare Analytics	122
45	Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography Tariq Mahmood, Tanzila Saba, Amjad Rehman et al. Expert Systems with Applications	120
46	CMUNEXT: An Efficient Medical Image Segmentation Network Based on Large Kernel and Skip Fusion Fenghe Tang, Jianrui Ding, Quan Quan et al. —	118
47	Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024 Alessandro Carriero, Léon Groenhoff, Elizaveta Vologina et al. Diagnostics	117
48	Cellpose3: one-click image restoration for improved cellular segmentation Carsen Stringer, Marius Pachitariu bioRxiv (Cold Spring Harbor Laboratory)	116
49	Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges Showrov Islam, M Aziz, Hadiur Rahman Nabil et al. IEEE Access	113
50	Deep learning-aided decision support for diagnosis of skin disease across skin tones Matthew Groh, Omar Badri, Roxana Daneshjou et al. Nature Medicine	112

Top Papers: KI in der Krebserkennung (2024)

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