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Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities.
The ‘ugly duckling’sign: identification of the common characteristics of nevi in an individual as a basis for melanoma screening
Grob, J. & Bonerandi, J · 1998
Earlier work this paper cites.
Comparison of dermatologic diagnoses by primary care practitioners and dermatologists: a review of the literature
Federman, D. G., Concato, J. & Kirsner, R. S · 1999
Earlier work this paper cites.
Diagnostic accuracy of dermoscopy
Kittler, H., Pehamberger, H., Wolff, K. & Binder, M · 2002
Earlier work this paper cites.
Identification of clinically featureless incipient melanoma using sequential dermoscopy imaging
Kittler, H. et al · 2006
Earlier work this paper cites.
Assessment of the optimal interval for and sensitivity of short-term sequential digital dermoscopy monitoring for the diagnosis of melanoma
Altamura, D., Avramidis, M. & Menzies, S. W · 2008
Earlier work this paper cites.
Fast explicit diffusion for accelerated features in nonlinear scale spaces
Alcantarilla, P. F. & Solutions, T · 2011
Earlier work this paper cites.
The cancer imaging archive (tcia): maintaining and operating a public information repository
Clark, K. et al · 2013
Earlier work this paper cites.
Risk prediction models for melanoma: a systematic review
Usher-Smith, J. A., Emery, J., Kassianos, A. P. & Walter, F. M · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O. et al · 2015
Earlier work this paper cites.
Cancer systems biology of tcga skcm: efficient detection of genomic drivers in melanoma
Guan, J., Gupta, R. & Filipp, F. V · 2015
Earlier work this paper cites.
Ph2: A public database for the analysis of dermoscopic images
Mendonça, T., Celebi, M., Mendonca, T. & Marques, J · 2015
Earlier work this paper cites.
Med-node: A computer-assisted melanoma diagnosis system using non-dermoscopic images
Giotis, I. et al · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S. & Gupta, A · 2017
Earlier work this paper cites.
Global burden of skin disease: inequities and innovations
Seth, D., Cheldize, K., Brown, D. & Freeman, E. E · 2017
Earlier work this paper cites.
Risk stratification for melanoma: models derived and validated in a purpose-designed prospective cohort
Olsen, C. M. et al · 2018
Earlier work this paper cites.
Multimodal skin lesion classification using deep learning
Yap, J., Yolland, W. & Tschandl, P · 2018
Earlier work this paper cites.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C. & Kittler, H · 2018
Earlier work this paper cites.
Global burden of cutaneous melanoma attributable to ultraviolet radiation in 2012
Arnold, M. et al · 2018
Earlier work this paper cites.
‘mind your moles’ study: protocol of a prospective cohort study of melanocytic naevi
Koh, U. et al · 2018
Earlier work this paper cites.
Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)
Codella, N. C. et al · 2018
Earlier work this paper cites.
Attention-based deep multiple instance learning
Ilse, M., Tomczak, J. & Welling, M · 2018
Earlier work this paper cites.
Seven-point checklist and skin lesion classification using multitask multimodal neural nets
Kawahara, J., Daneshvar, S., Argenziano, G. & Hamarneh, G · 2018
Earlier work this paper cites.
Evaluation of the efficacy of 3d total-body photography with sequential digital dermoscopy in a high-risk melanoma cohort: protocol for a randomised controlled trial
Primiero, C. A. et al · 2019
Earlier work this paper cites.
Milton, M. A. A · 2019
Earlier work this paper cites.
A deep learning system for differential diagnosis of skin diseases
Liu, Y. et al · 2020
Earlier work this paper cites.
A survey on contrastive self-supervised learning
Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D. & Makedon, F · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Kaplan, J. et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A · 2020
Cited alongside, same era.
Pad-ufes-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
Pacheco, A. G. et al · 2020
Cited alongside, same era.
Short-term lesion change detection for melanoma screening with novel siamese neural network
Zhang, B. et al · 2020
Cited alongside, same era.
Human–computer collaboration for skin cancer recognition
Tschandl, P. et al · 2020
Cited alongside, same era.
Zhang, S. et al · 2023
Later among the works it cites.
Dermnet (2023)
Dermnet · 2023
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Towards trustable skin cancer diagnosis via rewriting model’s decision
Yan, S. et al · 2023
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Cae v2: Context autoencoder with clip latent alignment
Zhang, X. et al · 2023
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Batformer: Towards boundary-aware lightweight transformer for efficient medical image segmentation
Lin, X., Yu, L., Cheng, K.-T. & Yan, Z · 2023
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A reinforcement learning model for ai-based decision support in skin cancer
Barata, C. et al · 2023
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Rapini, R. P · 2021
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Big self-supervised models advance medical image classification
Azizi, S. et al · 2021
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Learning transferable visual models from natural language supervision
Radford, A. et al · 2021
Cited alongside, same era.
Skin melanoma deaths within 1 or 3 years from diagnosis in europe
Sacchetto, L. et al · 2021
Cited alongside, same era.
Early melanoma diagnosis with sequential dermoscopic images
Yu, Z. et al · 2021
Cited alongside, same era.
Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset
Groh, M. et al · 2021
Cited alongside, same era.
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y. et al · 2021
Cited alongside, same era.
Later among the works it cites.
Towards reliable dermatology evaluation benchmarks
Groger, F. et al · 2023
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Automated photodamage assessment from 3d total body photography for an objective assessment of melanoma risk
Kahler, S. et al · 2023
Later among the works it cites.
A narrative review: opportunities and challenges in artificial intelligence skin image analyses using total body photography
Primiero, C. A. et al · 2024
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A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification
Primiero, C. A. et al · 2024
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Dermatoscopic patterns of cutaneous metastases: A multicentre cross‐sectional study of the international dermoscopy society
Tiodorovic, D. et al · 2024
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Prediction of melanoma metastasis using dermatoscopy deep features: An international multicenter cohort study
Lallas, K. et al · 2024
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The promises and perils of foundation models in dermatology
Gui, H., Omiye, J. A., Chang, C. T. & Daneshjou, R · 2024
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Foundation model for cancer imaging biomarkers
Pai, S. et al · 2024
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A whole-slide foundation model for digital pathology from real-world data
Xu, H. et al · 2024
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Towards a general-purpose foundation model for computational pathology
Chen, R. J. et al · 2024
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A foundation model for clinical-grade computational pathology and rare cancers detection
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A pathology foundation model for cancer diagnosis and prognosis prediction
Wang, X. et al · 2024
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Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
Shen, Y. et al · 2024
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DINOv2: Learning robust visual features without supervision
Oquab, M. et al · 2024
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Transparent medical image ai via an image–text foundation model grounded in medical literature
Kim, C. et al · 2024
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A generalist vision–language foundation model for diverse biomedical tasks
Zhang, K. et al · 2024
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The slice-3d dataset: 400,000 skin lesion image crops extracted from 3d tbp for skin cancer detection
Kurtansky, N. R. et al · 2024
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Segment anything in medical images
Ma, J. et al · 2024
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Deep learning-aided decision support for diagnosis of skin disease across skin tones
Groh, M. et al · 2024
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Bcn20000: Dermoscopic lesions in the wild
Hernández-Pérez, C. et al · 2024
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Yan, S. et al · 2025
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