ISIC skin lesion archive

data category: Medical and clinical datasets · 0 papers in the last 7 days

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Clinical records, medical images and physiological signals used in medical machine learning.

Names searched for: ISIC 2018, ISIC 2019, ISIC 2020, ISIC archive, ISIC challenge

Key findings#

From Nov 2023 to Oct 2026, 71 archived papers mentioned ISIC skin lesion archive in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Aug 2024 (6).
No paper of the tracked journals mentioned ISIC skin lesion archive in the last 30 days (2026-10-07 to 2026-11-05).
From 2017 to 2026 (so far), ISIC skin lesion archive was mentioned in 97 papers in the tracked journals, most of them in AI (95), marketing (1) and statistics (1).
26 of them were published in 2025, against 0 in 2017.
Since 2023, the institutions with the most of these papers were COMSATS University Islamabad (3), Chinese University of Hong Kong (3) and City University of Hong Kong (3).
The journals publishing most of them were Neural Computing and Applications (11), Artificial Intelligence Review (9) and Expert Systems with Applications (9).
The most-cited of them is “Medical Image Segmentation Review: The Success of U-Net” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024), with 1,065 citations.
Papers use ISIC skin lesion archive most often with ImageNet (31), CIFAR-10 / CIFAR-100 (12) and MNIST / Fashion-MNIST (11); it has 14 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

Mentions over 36 months#

Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)
Trend	Nov 23–Oct 24	Nov 24–Oct 25	Nov 25–Oct 26	36 months
	25	23	23	71

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	2	7	10	12	21	26	17
Marketing	0	0	0	0	1	0	0
Statistics	0	0	0	0	1	0	0
All categories	2	7	10	12	23	26	17

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	Neural Computing and Applications	11
2	Artificial Intelligence Review	9
3	Expert Systems with Applications	9
4	Pattern Recognition	8
5	Journal Of Big Data	7
6	Neural Networks	3
7	International Journal of Computer Vision	3
8	Applied Intelligence	3
9	IEEE Transactions on Neural Networks and Learning Systems	3
10	IEEE Transactions on Artificial Intelligence	3

Institutions with the most papers, since 2023
#	Institution	Papers
1	COMSATS University Islamabad	3
2	Chinese University of Hong Kong	3
3	City University of Hong Kong	3
4	Indian Statistical Institute	3
5	Agency for Science, Technology and Research	2
6	De Montfort University	2
7	Hong Kong Polytechnic University	2
8	Imperial College London	2
9	Institute of High Performance Computing	2
10	Iğdır Üniversitesi	2

Countries with the most papers, since 2023
#	Country	Papers
1	China	34
2	United Kingdom	12
3	India	11
4	United States	11
5	Turkey	7
6	Canada	6
7	Australia	5
8	Saudi Arabia	5
9	Hong Kong	4
10	Germany	3

Keywords with the most papers, since 2023
#	Keyword	Papers
1	ISIC 2018	18
2	skin lesion segmentation	15
3	medical image segmentation	13
4	skin lesion classification	13
5	ISIC 2017	9
6	ISIC 2019	9
7	skin cancer diagnosis	9
8	medical image classification	8
9	dermoscopic images	7
10	federated learning	7

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	1,065	40.2	Medical Image Segmentation Review: The Success of U-Net	IEEE Transactions on Pattern Analysis and Machine Intelligence	2024-08-21	Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland et al.
2	522	16.4	Federated learning for medical image analysis: A survey	Pattern Recognition	2024-03-12	Hao Guan, Pew‐Thian Yap, Andrea Bozoki et al.
3	495	14.0	A comprehensive survey on applications of transformers for deep learning tasks	Expert Systems with Applications	2023-11-22	Saidul Islam, Hanae Elmekki, Ahmed Elsebai et al.
4	338	4.9	Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks	IEEE Transactions on Neural Networks and Learning Systems	2021-01-14	K. Ruwani M. Fernando, Chris P. Tsokos
5	323	4.2	Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art	Artificial Intelligence Review	2020-06-27	Adekanmi Adeyinka Adegun, Serestina Viriri

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

Used together with#

Papers use ISIC skin lesion archive most often with ImageNet (31), CIFAR-10 / CIFAR-100 (12) and MNIST / Fashion-MNIST (11); it has 14 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST. The whole network →

What papers use ISIC skin lesion archive together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
ImageNet	Dataset	31	6.5
CIFAR-10 / CIFAR-100	Dataset	12	4.3
MNIST / Fashion-MNIST	Dataset	11	4.9
BraTS	Dataset	10	18.1
Kaggle	Dataset	10	8.6
TCGA	Dataset	8	21.8
Chest X-ray (CheXpert, ChestX-ray14)	Dataset	5	25.7
Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers)	Dataset	5	10.8
LIDC-IDRI	Dataset	5	37.4
MS COCO	Dataset	5	2.7
ADNI	Dataset	4	8.1
SVHN	Dataset	4	9.4

Cite this page

Scholar4AI. (2026). ISIC skin lesion archive. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/isic-skin-lesion-archive

@misc{scholar4ai-datasets-isic-skin-lesion-archive,
  author = {{Scholar4AI}},
  title = {ISIC skin lesion archive},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/isic-skin-lesion-archive}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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