Top Papers: KI in der Krebserkennung (2026)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2026 (von 112 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	CellViT++: Energy-efficient and adaptive cell segmentation and classification using foundation models Fabian Horst, Moritz Rempe, Helmut Becker et al. Computer Methods and Programs in Biomedicine	5
3	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
4	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	4
5	AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer Zhe Li, Yuchen Li, Jinxi Xiang et al. Nature Medicine	3
6	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
7	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
8	Deep learning‐based ecological analysis of camera trap images is impacted by training data quality and quantity Omiros Pantazis, Peggy A. Bevan, Holly Pringle et al. Remote Sensing in Ecology and Conservation	3
9	Clinical-grade autonomous cytopathology through whole-slide edge tomography Nao Nitta, Yuko Sugiyama, Takeaki Sugimura et al. Nature	2
10	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
11	Explainable Multi-Modal Medical Image Analysis Through Dual-Stream Multi-Feature Fusion and Class-Specific Selection Naeem Ullah, Ivanoe De Falco, Giovanna Sannino AI	2
12	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
13	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
14	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
15	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
16	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
17	CAMP: continuous and adaptive learning model in pathology Anh Tien Nguyen, Keunho Byeon, Kyungeun Kim et al. npj Artificial Intelligence	2
18	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
19	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
20	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
21	Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation Nadezhda Alsahanova, Pavel Bartenev, Maxim Sharaev et al. Studies in computational intelligence	2
22	Enhancing histopathological image classification via integrated HOG and deep features with robust noise performance Ifeanyi Ezuma, Ugochukwu Ugwu —	1
23	Debunking Optimization Myths in Federated Learning for Medical Image Classification Youngjoon Lee, Hyukjoon Lee, Jinu Gong et al. Lecture notes in computer science	1
24	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
25	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
26	From modality-specific to compositional foundation models for cell biology Mojtaba Bahrami, Till Richter, Niklas A. Schmacke et al. Cell Systems	1
27	Finding the optimal recall rate in breast cancer screening: results from the ROCS study Daniëlle van der Waal, Craig K. Abbey, Cees Haaring et al. European Radiology	1
28	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
29	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
30	Deep visual detection system for oral squamous cell carcinoma K. Akram, Muhammad Aslam, Talha Waheed et al. Scientific Reports	1
31	A two-stage self-supervised learning framework for breast cancer detection with multi-scale vision transformers Shahriar Mohammadi, Mohammad Ahmadi Livani Information Sciences	1
32	Semi-supervised blastocyst image segmentation via topology-aware self-distillation framework Linwei Qiu, Hua Wang, J Wang et al. Expert Systems with Applications	1
33	Prospective evaluation of artificial intelligence integration into breast cancer screening in multiple workflow settings: the GEMINI study Clarisse F. de Vries, Gerald Lip, Roger Todd Staff et al. Nature Cancer	1
34	Realising precision oncology through shared real-world data infrastructure Andreas Bjerrum, Andreas Fanø, Ulrik Lassen Acta Oncologica	1
35	Semi-supervised dual-teacher comparative learning with bidirectional balanced copy-paste for medical image segmentation Jiangxiong Fang, Song Qi, Youyao Fu et al. Biomedical Signal Processing and Control	1
36	Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment Shiyun Chen, Li Lin, Pujin Cheng et al. Lecture notes in computer science	1
37	Geometric multi-instance learning for weakly supervised gastric cancer segmentation Chenshen Huang, Haoyun Xia, Xi Xiao et al. npj Digital Medicine	1
38	Optimized CNN framework with VGG19, EfficientNet, and Bayesian optimization for early colon cancer detection Tawfikur Rahman, Nibedita Deb, Samia Larguech et al. Scientific Reports	1
39	CRViT-YOLO: A method for multi-morphological blood cell detection using convolution-restructured vision transformer Yong Du, Yuliang Ma, Qingshan She et al. Tissue and Cell	1
40	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	1
41	Minimum Data, Maximum Impact: 20 Annotated Samples for Explainable Lung Nodule Classification Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson et al. Lecture notes in computer science	1
42	The next layer: augmenting foundation models with structure-preserving and attention-guided learning for local patches to global context awareness in computational pathology Muhammad Waqas, Rukhmini Bandyopadhyay, Eman Showkatian et al. npj Precision Oncology	1
43	xMagNet: Dynamic magnification-aware fusion with uncertainty quantification for robust breast cancer histopathology Saeed Iqbal, Muhammad Attique Khan, Leila Jamel et al. Neurocomputing	1
44	Multimodal large language models for oral lesion diagnosis: a systematic review of diagnostic performance and clinical utility Fatma E. A. Hassanein, Malik Alkabazi, Melek Taşsöker et al. Frontiers in Oral Health	1
45	AI in hematology: A new frontier for nursing practice and patient care Abdelrahman M. Nasiri, Nebras A. Alsalman, Fai A. Aljandabi et al. Journal of Family Medicine and Primary Care	1
46	AI-generated data contamination erodes pathological variability and diagnostic reliability Hongyu He, Shaowen Xiang, Y. P. Zhang et al. —	1
47	Lightweight deep learning model with spatial attention for accurate and efficient breast cancer prediction Jaafar Jaafari, Hind Ezzine, Khadija Douzi et al. Scientific Reports	1
48	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	1
49	Multimodal Phasor Analysis for Digital Pathology: Quantitative Characterization of Liver Iron Overload Davide Panzeri, Reha Akpınar, Laura D’Alfonso et al. Chemical & Biomedical Imaging	1
50	Energy consumption of standard and contrast-enhanced mammography: a step towards sustainable breast imaging Gabriele Rossini, Kieran Lockey, Maxime Rokoszak et al. European Radiology	1

Top Papers: KI in der Krebserkennung (2026)

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