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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2506.06099 |
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| _version_ | 1866918277121310720 |
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| author | Madarkar, Shanawaj S Madarkar, Mahajabeen Venkatesh, Madhumitha Bansal, Deepanshu Prakash, Teli Mopuri, Konda Reddy MV, Vinaykumar Sathwika, KVL Kasturi, Adarsh Raj, Gandla Dilip Supranitha, PVN Udai, Harsh |
| author_facet | Madarkar, Shanawaj S Madarkar, Mahajabeen Venkatesh, Madhumitha Bansal, Deepanshu Prakash, Teli Mopuri, Konda Reddy MV, Vinaykumar Sathwika, KVL Kasturi, Adarsh Raj, Gandla Dilip Supranitha, PVN Udai, Harsh |
| contents | Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively curated dermatology dataset comprising 5,450 clinical images from 3,002 patients across outpatient clinics in South India. Each image is annotated by board-certified dermatologists with 245 distinct diagnoses, structured under a hierarchical, aetiology-based taxonomy adapted from Rook's classification. The dataset captures a wide spectrum of dermatologic conditions and tonal variation commonly seen in Indian outpatient care. We benchmark a range of architectures, including convolutional models (ResNet, DenseNet, EfficientNet), transformer-based models (ViT, MaxViT, Swin), and Concept Bottleneck Models to establish baseline performance and explore how anatomical and concept-level cues may be integrated. These results are intended to guide future efforts toward interpretable and clinically realistic models. DermaCon-IN provides a scalable and representative foundation for advancing dermatology AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research Madarkar, Shanawaj S Madarkar, Mahajabeen Venkatesh, Madhumitha Bansal, Deepanshu Prakash, Teli Mopuri, Konda Reddy MV, Vinaykumar Sathwika, KVL Kasturi, Adarsh Raj, Gandla Dilip Supranitha, PVN Udai, Harsh Image and Video Processing Computer Vision and Pattern Recognition Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively curated dermatology dataset comprising 5,450 clinical images from 3,002 patients across outpatient clinics in South India. Each image is annotated by board-certified dermatologists with 245 distinct diagnoses, structured under a hierarchical, aetiology-based taxonomy adapted from Rook's classification. The dataset captures a wide spectrum of dermatologic conditions and tonal variation commonly seen in Indian outpatient care. We benchmark a range of architectures, including convolutional models (ResNet, DenseNet, EfficientNet), transformer-based models (ViT, MaxViT, Swin), and Concept Bottleneck Models to establish baseline performance and explore how anatomical and concept-level cues may be integrated. These results are intended to guide future efforts toward interpretable and clinically realistic models. DermaCon-IN provides a scalable and representative foundation for advancing dermatology AI. |
| title | DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.06099 |