Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

By David Gutman, Noel C. F. Codella, Emre M. Celebi, Brian Helba, Michael Marchetti, N. Mishra, Allan Halpern. arXiv.org, 2016

870 citations recorded by the source corpus.

In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.

Subfields: Oncology

View Paper: https://www.semanticscholar.org/paper/dd285594ee34fb46b2afdc1b40c6b7993d607d65

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In Oncology

1. P. Heng
2. Hao Chen
3. Allan Halpern
4. Noel C. F. Codella
5. Brian Helba
6. Lequan Yu
7. John R. Smith
8. Huan Fu

Citations in the corpus

• Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance (2017, methodology): “One is provided by the International Skin Imaging Collaboration (ISIC) for the 2016 International Symposium on Biomedical Imaging (ISBI 2016) challenge titled “Skin lesion analysis toward melanoma detection” [29].”
• Attention Residual Learning for Skin Lesion Classification (2019, positive): “We evaluated the proposed ARL-CNN model on the ISICskin 2017 dataset [5] which is a largest publicly available skin dermoscopy image achieve, and achieved the state-of-the-art performance (i.”
• Transformers in Medical Imaging: A Survey (2022, methodology): “Extensive experiments on ISIC 2016+PH2 [99], [100] and ISIC 2018 [101] validate the efficacy of their boundary-wise prior, as shown in Fig.”
• Segmentation of Both Diseased and Healthy Skin From Clinical Photographs in a Primary Care Setting (2018, background): “Biomedical Imaging (ISBI) in 2016 and 2017 around the analysis of dermoscopic images have devoted an entire task toward segmentation [3].”
• BCN20000: Dermoscopic Lesions in the Wild (2019, background): “A significant player in the adoption of these algorithms in the community has been the International Skin Imaging Collaboration (ISIC), which has been organizing yearly challenges since 2016, where participants are asked to develop computer vision algorithms to segment and classify skin lesions in d”
• 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) (2016, methodology): “Similar to the prior year challenge [16], the 2017 challenge”
• AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection (2023, neutral): “In medical imaging, we consider skin cancer detection dataset ISIC (Gutman et al., 2016), colon polyp detection datasets CVC-ClinicDB (Bernal et al., 2015), CVC-ColonDB (Tajbakhsh et al., 2015), Kvasir (Jha et al., 2020), and Endo (Hicks et al., 2021), thyroid nodule detection dataset TN3k (Gong et…”
• Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017, methodology): “Results: We achieved an average Dice coefficient of 91.18% on the ISBI 2016 Skin Lesion Challenge dataset and 90.66% on the PH2 dataset.”
• Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (2017, methodology): “We performed extensive experiments to evaluate our method on a public challenge dataset of Skin Lesion Analysis Towards Melanoma Detection released with ISBI 2016 [41].”
• Channel prior convolutional attention for medical image segmentation (2023, methodology): “, 2018) and (b) Skin lesion segmentation (Gutman et al., 2016; Barata et al., 2014).”
• Machine Learning Methods for Histopathological Image Analysis (2017, neutral): “[77] Gutman D, Codella NCF, Celebi E, Helba B, Marchetti M, Mishra N, et al. Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC).”