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Topic · Radiology, Nuclear Medicine and Imaging

Radiomics and Machine Learning in Medical Imaging

Radiomics and Machine Learning in Medical Imaging is a research topic within Radiology, Nuclear Medicine and Imaging. Science Explorer counts 71k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of radiomics, a quantitative analysis of medical images, particularly in the context of cancer imaging and precision medicine. It explores the extraction of advanced features from medical images, the use of machine learning for predictive modeling, and the assessment of tumor heterogeneity through texture analysis.

  • Radiomics
  • Medical Imaging
  • Quantitative Analysis
  • Feature Extraction
  • Tumor Heterogeneity
  • Machine Learning
  • Cancer Imaging
  • Predictive Modeling
  • Texture Analysis
  • Precision Medicine
Research works
71k
fractional, since 1950
In the world top 10%
7.2k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+69%
the tick is no change
Largest producer
China
36% of the 10 largest, 2022-now
Radiomics and Machine Learning in Medical Imaging

This ranks research output and citation impact. It says nothing about the quality of diagnosis, treatment or care.

Which countries lead Radiomics and Machine Learning in Medical Imaging research?

By volume, China and the United States publish the most (7.6k and 5.2k works in 2022-now).

By volume, 2022-now

  1. 1 China 7.6k works
  2. 2 United States 5.2k works
  3. 3 India 2.6k works
  4. 4 United Kingdom 1.1k works
  5. 5 Germany 1.1k works
  6. 6 Italy 928 works
  7. 7 Japan 815 works
  8. 8 South Korea 727 works
  9. 9 Canada 545 works
  10. 10 France 543 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 36.0%United States: 24.6%India: 12.4%United Kingdom: 5.1%6 others listed: 21.9%36%largest
China7,594 · 36.0%United States5,194 · 24.6%India2,615 · 12.4%United Kingdom1,082 · 5.1%6 others listed4,614 · 21.9%

Shares of the rows listed above, not of the whole node.

Which institutions lead Radiomics and Machine Learning in Medical Imaging research?

By volume in 2022-now, Sun Yat-sen University publishes the most Radiomics and Machine Learning in Medical Imaging research, followed by Harvard University and Shanghai Jiao Tong University.

By volume, 2022-now

  1. 1 Sun Yat-sen UniversityChina 265 works
  2. 2 Harvard UniversityUnited States 242 works
  3. 3 Shanghai Jiao Tong UniversityChina 197 works
  4. 4 Fudan UniversityChina 155 works
  5. 5 Sichuan UniversityChina 153 works
  6. 6 Mayo ClinicUnited States 148 works
  7. 7 Peking UniversityChina 141 works
  8. 8 The University of Texas MD Anderson Cancer CenterUnited States 139 works
  9. 9 Zhejiang UniversityChina 130 works
  10. 10 Chinese Academy of Medical Sciences & Peking Union Medical CollegeChina 127 works

Who are the leading researchers in Radiomics and Machine Learning in Medical Imaging?

The most-cited researchers publishing on Radiomics and Machine Learning in Medical Imaging include David Moher, Douglas G. Altman and Ahmedin Jemal.

  1. 1 David Moher Canada 898k citations
  2. 2 Douglas G. Altman United Kingdom 862k citations
  3. 3 Ahmedin Jemal United States 761k citations
  4. 4 Yoshua Bengio Canada 476k citations
  5. 5 Rob Knight United States 468k citations
  6. 6 Stacey L. Gabriel United States 402k citations
  7. 7 Robert John Tibshirani United States 396k citations
  8. 8 Bert Vogelstein United States 373k citations
  9. 9 Gad A. Getz United States 364k citations
  10. 10 Elaine R. Mardis United States 346k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Radiomics and Machine Learning in Medical Imaging?

The most cited paper in Radiomics and Machine Learning in Medical Imaging is "A survey on deep learning in medical image analysis" (Geert J. S. Litjens, Thijs Kooi, Babak Ehteshami Bejnordi et al., 2017), with 15,309 citations. Next come "High-performance medicine: the convergence of human and artificial intelligence" (10,125) and "UNet++: A Nested U-Net Architecture for Medical Image Segmentation" (9,545).

Most cited papers
#PaperReleasedCitations
1 A survey on deep learning in medical image analysis Geert J. S. Litjens, Thijs Kooi, Babak Ehteshami Bejnordi et al. · Medical Image Analysis Open access 26 Jul 2017 15,309
2 High-performance medicine: the convergence of human and artificial intelligence Eric J. Topol · Nature Medicine 28 Dec 2018 10,125
3 UNet++: A Nested U-Net Architecture for Medical Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh et al. · Lecture notes in computer science Conference paperOpen access 2018 9,545
4 3D Slicer as an image computing platform for the Quantitative Imaging Network Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy–Cramer et al. · Magnetic Resonance Imaging Open access 7 Jul 2012 9,292
5 QuPath: Open source software for digital pathology image analysis Peter Bankhead, Maurice Bernard Loughrey, José A. Fernández et al. · Scientific Reports Open access 28 Nov 2017 9,140
All 100 most cited papers, with filters and sorting

Where is Radiomics and Machine Learning in Medical Imaging research done?

The largest centres of Radiomics and Machine Learning in Medical Imaging research in 2022-now are Beijing (China), Shanghai (China), Guangzhou (China) and Seoul (South Korea). Among places with at least 20 works in it, it is an unusually large share of all research in Amsterdam, Plainview and Rozzano.

Largest cities, 2022-now

  1. 1 BeijingChina 857 works
  2. 2 ShanghaiChina 755 works
  3. 3 GuangzhouChina 582 works
  4. 4 SeoulSouth Korea 426 works
  5. 5 LondonUnited Kingdom 369 works
  6. 6 ChennaiIndia 337 works
  7. 7 New YorkUnited States 329 works
  8. 8 HangzhouChina 311 works
  9. 9 ChengduChina 297 works
  10. 10 NanjingChina 271 works

Where it is the local speciality

  1. AmsterdamNL · 21.1 works18×
  2. PlainviewUS · 29.3 works14×
  3. RozzanoIT · 38.8 works9.4×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Radiomics and Machine Learning in Medical Imaging than the world average.

See Radiomics and Machine Learning in Medical Imaging on the map

Where is the best place to study Radiomics and Machine Learning in Medical Imaging?

Among universities, judged by research, Princess Nourah bint Abdulrahman University, Harvard University and Stanford University score highest, combining citation excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 15.73%fractional works in this field (log) →share of work in the world top 10% →Princess Nourah bint…Stanford UniversityHarvard UniversitySun Yat-sen University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Citation excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Princess Nourah bint Abdulrahman UniversitySaudi Arabia 66.634.3%2.4×14 +674.5%
2Harvard UniversityUnited States 63.914.6%3.2×242 +11.3%
3Stanford UniversityUnited States 60.619.6%2.9×104 +29.0%
4Sun Yat-sen UniversityChina 60.410.6%5.4×265 +72.3%
5Heidelberg UniversityGermany 57.914.8%5.2×96 +64.1%
6Radboud University NijmegenNetherlands 56.517.2%3.6×51 +9.4%
7Seoul National UniversitySouth Korea 54.713.7%3.5×94 +21.0%
8Sichuan UniversityChina 54.212.3%2.9×153 +45.4%
9Shanghai Jiao Tong UniversityChina 53.910.0%2.9×197 +61.4%
10Fudan UniversityChina 53.710.2%4.1×155 +64.7%

Universities only. The score blends citation excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Radiomics and Machine Learning in Medical Imaging, country by country

Is Radiomics and Machine Learning in Medical Imaging research growing?

Output in 2022-2025 was 69% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Radiomics and Machine Learning in Medical Imaging.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Radiomics and Machine Learning in Medical Imaging as a percentage of every work published that year, 0.13% in 2024.

still counting0.050%0.10%0.15%Radiomics and Machine Learning in Medical Imaging0.13% in 202519801990200020102020

Fractional works of Radiomics and Machine Learning in Medical Imaging over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Radiomics and Machine Learning in Medical Imaging9.9% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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