Leaderboards/Radiomics and Machine Learning in Medical Imaging

Radiomics and Machine Learning in Medical Imaging

Last updated July 15, 2026

Rising Stars

Fresh ink, massive impact

Researchers

1
Hanwen Xu
University of Washington
2
Patrick Bilic
Technical University of Munich
3
Paul Hager
University Hospital Ulm
4
Seyed Masoud Ghoreishi Mokri
Privolzhsky Research Medical University
5
Jorge Luis Galeano Niño
The University of Texas at El Paso
6
Abdelrahman Shaker
Mohamed bin Zayed University of Artificial Intelligence
7
Jinxi Xiang
Stanford University
8
Along He
Shenzhen Technology University
9
Sandra Steyaert
Delta Air Lines (United States)
10
Kiran Jabeen
HITEC University

Papers

1
A whole-slide foundation model for digital pathology from real-world data
Nature|2024
652 cit.
2
Evaluation and mitigation of the limitations of large language models in clinical decision-making
Nature Medicine|2024
570 cit.
3
Using Convolutional Neural Network to Design and Predict the Forces and Kinematic Performance and External Rotation Moment of the Hip Joint in the Pelvis
International Journal of Innovative Science and Research Technology (IJISRT)|2024
511 cit.
4
UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation
IEEE Transactions on Medical Imaging|2024
362 cit.
5
Predicting the Performance and Adaptation of Artificial Elbow Due to Effective Forces using Deep Learning
International Journal of Innovative Science and Research Technology (IJISRT)|2024
275 cit.
6
Using RNN Artificial Neural Network to Predict the Occurrence of Gastric Cancer in the Future of the World
International Journal of Innovative Science and Research Technology (IJISRT)|2024
235 cit.
7
Diagnosis and Evaluation of Stomach Surgery with CNN Neural Network
International Journal of Innovative Science and Research Technology (IJISRT)|2024
234 cit.
8
Publication in Nature (2025) [169 citations, W4406168947]
Nature|2025
169 cit.
9
Publication in Frontiers in Oncology (2024) [36 citations, W4392056032]
Frontiers in Oncology|2024
36 cit.
10
Publication in Engineering Applications of Artificial Intelligence (2024) [27 citations, W4401732046]
Engineering Applications of Artificial Intelligence|2024
27 cit.

Emerging Leaders

Drafting the new consensus

Researchers

1
Leonid Kvecher
Windber Research Institute
2
Daguang Xu
Nvidia (United States)
3
Shravya Shetty
Google (United States)
4
Özal Yıldırım
Fırat University
5
Tzani A. Mpesiana
University of Patras
6
Daniel Tse
Google (United States)
7
Jakob Nikolas Kather
Heidelberg University
8
Geert Litjens
Radboud University Nijmegen
9
Colin Jacobs
Radboud University Nijmegen
10
Matthew P. Lungren
Microsoft (United States)

Papers

1
UNETR: Transformers for 3D Medical Image Segmentation
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)|2022
2.9k cit.
2
Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study
Cancer Cell|2023
247 cit.
3
Swarm learning for decentralized artificial intelligence in cancer histopathology
Nature Medicine|2022
209 cit.
4
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
Medical Image Analysis|2022
173 cit.
5
In-context learning enables multimodal large language models to classify cancer pathology images
Nature Communications|2024
132 cit.
6
Adversarial attacks and adversarial robustness in computational pathology
Nature Communications|2022
110 cit.
7
Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology
Nature Cancer|2025
108 cit.
8
Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application
ESMO Open|2022
107 cit.
9
Extracting structured information from unstructured histopathology reports using generative pre‐trained transformer 4 ( <scp>GPT</scp> ‐4)
The Journal of Pathology|2023
106 cit.
10
Deep learning in cancer genomics and histopathology
Genome Medicine|2024
101 cit.

Current Senior Leaders

Wrote the book, still writing it

Researchers

1
Jianming Liang
Arizona State University
2
Hugo J.W.L. Aerts
Harvard University
3
Philippe Lambin
Maastricht University Medical Centre
4
Ron Kikinis
Brigham and Women's Hospital
5
Ronald M. Summers
National Institutes of Health Clinical Center
6
Manuel Salto‐Tellez
Queen's University Belfast
7
Hedvig Hricak
Memorial Sloan Kettering Cancer Center
8
Anant Madabhushi
Emory University
9
Tony Yu
University of Chicago
10
Dinggang Shen
ShanghaiTech University

Papers

1
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Lecture notes in computer science|2018
9k cit.
2
3D Slicer as an image computing platform for the Quantitative Imaging Network
Magnetic Resonance Imaging|2012
8.9k cit.
3
Radiomics: Images Are More than Pictures, They Are Data
Radiology|2015
8.3k cit.
4
scikit-image: image processing in Python
PeerJ|2014
6.9k cit.
5
Computational Radiomics System to Decode the Radiographic Phenotype
Cancer Research|2017
6.5k cit.
6
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
IEEE Transactions on Medical Imaging|2016
5.8k cit.
7
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Nature Communications|2014
5.1k cit.
8
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
IEEE Transactions on Medical Imaging|2019
4.1k cit.
9
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
IEEE Transactions on Medical Imaging|2016
3.2k cit.
10
Statistical validation of image segmentation quality based on a spatial overlap index1
Academic Radiology|2004
1.9k cit.

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