Top Papers: KI in der Krebserkennung (2026)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2026 (von 191 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.

2024 2025 2026

#	Paper	Zitationen
1	Hermes: A research project on human sequence evaluation Jordi Gonzàlez, F. Xavier Roca, Juan J. Villanueva —	11
2	Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial Jessie Gommers, Veronica Hernström, Viktoria Josefsson et al. The Lancet	9
3	AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer Zhe Li, Yuchen Li, Jinxi Xiang et al. Nature Medicine	6
4	CellViT++: Energy-efficient and adaptive cell segmentation and classification using foundation models Fabian Hörst, Moritz Rempe, Helmut Becker et al. Computer Methods and Programs in Biomedicine	6
5	A Unified CNN-Based Instance Segmentation Architecture for Blood Cell Classification and Early Cancer Abnormality Recognition Nathaniel H. Dumayas, Rhomwell Ace C. Merced, Kenniniah A. Rit et al. —	6
6	Iris Fractal & Nevi Analysis: Comparative Study of Pigment Architecture and Pathology Markers — Argira Station (v5.1) Jose Ranero García Zenodo (CERN European Organization for Nuclear Research)	4
7	Explainable Multi-Modal Medical Image Analysis Through Dual-Stream Multi-Feature Fusion and Class-Specific Selection Naeem Ullah, Ivanoe De Falco, Giovanna Sannino AI	3
8	Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology Bitao Jiang, Yuefei Wu, Xiao Chen et al. Critical Reviews in Oncology/Hematology	3
9	GenoPath-MCA: Multimodal masked cross-attention between genomics and pathology for survival prediction Kaixuan Zhang, Shuqi Dong, Peifeng Shi et al. Computerized Medical Imaging and Graphics	3
10	Exploiting Scale-Variant Attention for Segmenting Small Medical Objects Wei Dai, Rui Liu, Zixuan Wu et al. IEEE Transactions on Neural Networks and Learning Systems	3
11	Deep learning‐based ecological analysis of camera trap images is impacted by training data quality and quantity Peggy A. Bevan, Omiros Pantazis, Holly Pringle et al. Remote Sensing in Ecology and Conservation	3
12	Clinical-grade autonomous cytopathology through whole-slide edge tomography Nao Nitta, Yuko Sugiyama, Takeaki Sugimura et al. Nature	2
13	Geometric multi-instance learning for weakly supervised gastric cancer segmentation Chenshen Huang, Haoyun Xia, Xi Xiao et al. npj Digital Medicine	2
14	Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches Shuo Gao, Jia Liu, Linqian Li et al. BioMedical Engineering OnLine	2
15	Optimized CNN framework with VGG19, EfficientNet, and Bayesian optimization for early colon cancer detection Tawfikur Rahman, Nibedita Deb, Samia Larguech et al. Scientific Reports	2
16	MRFF-DSPP-RI U-Net: Renal tumor segmentation using multiresolution feature fusion model based on enhanced u-net with dilated spatial pyramid pooling Chintam Anusha, K. Srinivasa Rao Biomedical Signal Processing and Control	2
17	Rethinking Multi-center Semi-supervised Breast Cancer Ultrasound Image Segmentation: An Intermediate-domain Perspective Zhaoyi Ye, Yimin Zhang, Jingyi Huang et al. IEEE Journal of Biomedical and Health Informatics	2
18	AI-generated data contamination erodes pathological variability and diagnostic reliability Hongyu He, Shaowen Xiang, Y. P. Zhang et al. medRxiv	2
19	GeoFed-Cervix: A Differential Geometry–Guided Federated and Explainable AI Framework for Early Cervical Cancer Detection on Consumer Devices Sabyasachi Mukhopadhyay, Nazeer Haider, Chinmay Chakraborty et al. IEEE Transactions on Consumer Electronics	2
20	Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges Fahad Ahmed, Naila Sammar Naz, Sunawar Khan et al. BMC Medical Imaging	2
21	Auditing shortcut learning and misclassification in artificial intelligence-based breast cancer genomic subtyping Julian Borges JAMIA Open	2
22	From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research Amgad Muneer, Muhammad Waqas, Maliazurina B. Saad et al. Artificial Intelligence Review	2
23	AI-FLEET: Phase I—Multimodal Deep Learning Model for Phyllodes Tumor Classification Logan Holt, Victoria Chamberlain, Tyler Shern et al. Annals of Surgical Oncology	2
24	Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review Kathryn P. Lowry, Han Eol Jeong, Ki Hwan Kim et al. JNCI Journal of the National Cancer Institute	2
25	Coevolutionary algorithms in generative adversarial networks for medical image analysis with limited labels Abdullah Al-Yaari, Youjun Deng, Muhammad Umar Abdullahi et al. Physica Scripta	2
26	Impact of using artificial intelligence as a second reader in breast screening including arbitration Lucy M. Warren, Jenny Venton, Kenneth C. Young et al. Nature Cancer	2
27	Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies Christopher Kelly, Marc Wilson, Lucy M. Warren et al. Nature Cancer	2
28	MicroDeblurNet: high-fidelity restoration of spatially variant defocus in microscopic images for cucumber downy mildew Yuqi Zhang, Bo Wang, Yuzhaobi Song et al. Plant Methods	2
29	CAMP: continuous and adaptive learning model in pathology Anh Tien Nguyen, Keunho Byeon, Kyungeun Kim et al. npj Artificial Intelligence	2
30	Confounding factors and biases abound when predicting molecular biomarkers from histological images Muhammad Dawood, Kim Branson, Sabine Tejpar et al. Nature Biomedical Engineering	2
31	Five-Year Absolute Risk–Based and Age-Based Breast Cancer Screening in the US O Alagoz, Yifan Lu, Eugenio Gil Quessep et al. JAMA Network Open	2
32	Prompt-level contrastive learning for context-aware multi-modal image representation in medical diagnosis Guowei Dai, Zhimin Tian, Chen Xin et al. Pattern Recognition	2
33	Developing a CR-SCAD algorithm for fibrosis and inflammatory activity analysis of chronic hepatitis C Jiaxin Cai, Siyu Liu, Bin Wang et al. International Journal of Machine Learning and Cybernetics	2
34	The automated computational workflow QUICHE reveals structural definitions of antitumor responses in triple-negative breast cancer Jolene S. Ranek, Noah F. Greenwald, Mako Goldston et al. Nature Cancer	2
35	Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation Nadezhda Alsahanova, Pavel Bartenev, Maxim Sharaev et al. Studies in computational intelligence	2
36	Enhancing histopathological image classification via integrated HOG and deep features with robust noise performance Ifeanyi Ezuma, Ugochukwu Ugwu —	1
37	Debunking Optimization Myths in Federated Learning for Medical Image Classification Youngjoon Lee, Hyukjoon Lee, Jinu Gong et al. Lecture notes in computer science	1
38	Agreement Across 10 Artificial Intelligence Models in Assessing Human Epidermal Growth Factor Receptor 2 (HER2) Expression in Breast Cancer Whole-Slide Images Brittany A. McKelvey, Pedro A Torres-Saavedra, J Li et al. Modern Pathology	1
39	Domain adaptation, self-supervision, and generative augmentation enhance GNNs for breast cancer prediction Shi Qiu, Yun Zhao, Xiuchang Li Scientific Reports	1
40	Computer-Aided Colonoscopy Alert Fatigue and Its Effect on Adenoma Detection Felix Huang, Victoire Michal, Roupen Djinbachian et al. The American Journal of Gastroenterology	1
41	Artificial intelligence for single-omics in ovarian cancer: a methodological review Pilar Ordás, José Luis Crossa, Luis M. Chiva International Journal of Gynecological Cancer	1
42	Prognostic Risk Refinement using Artificial Intelligence in HR+/HER2- Early Breast Cancer: Implications for CDK4/6 Eligibility Criteria Nicholas P. McAndrew, C. Ma, Andrew A Davis et al. medRxiv	1
43	Bridging the Gap Between Theoretical Performance and Clinical Utility in Multi-Class Skin Lesion Diagnosis Furkan Sönmez, Fevzi Das Artificial Intelligence in Applied Sciences	1
44	From modality-specific to compositional foundation models for cell biology Mojtaba Bahrami, Till Richter, Niklas A. Schmacke et al. Cell Systems	1
45	Machine Learning in Clinical Decision Making: Applications, Data Limitations and Multidisciplinary Perspectives Augusta Raţiu, Emilia-Loredana Pop Applied Sciences	1
46	Finding the optimal recall rate in breast cancer screening: results from the ROCS study Daniëlle van der Waal, Craig K. Abbey, Eric Tetteroo et al. European Radiology	1
47	A multi-expert deep learning framework with LLM-guided arbitration for multimodal histopathology prediction Shyam Sundar Debsarkar, V.B. Surya Prasath Computerized Medical Imaging and Graphics	1
48	AI-based BRAIx risk score for the intermediate-term prediction of breast cancer: a population cohort study Helen M L Frazer, John L Hopper, Tuong L. Nguyen et al. The Lancet Digital Health	1
49	Deep visual detection system for oral squamous cell carcinoma K. Akram, Muhammad Aslam, Talha Waheed et al. Scientific Reports	1
50	A two-stage self-supervised learning framework for breast cancer detection with multi-scale vision transformers Shahriar Mohammadi, Mohammad Ahmadi Livani Information Sciences	1

Top Papers: KI in der Krebserkennung (2026)

Verwandte Seiten