Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variability, and subtle visual differences between benign and malignant cases. Existing CNN/ViT pipelines typically rely on global or patch-level features and often combine patient metadata via late fusion, which limits spatially grounded multimodal reasoning. We present a novel region-based graph learning framework that explicitly models lesions as graphs of spatially coherent superpixel regions represented as frozen CNN features. To capture fine-grained lesion arrangements, we encode inter-regional geometry as edge attributes and introduce a dedicated metadata context node connected to all regions, providing structured integration of demographic/clinical variables within the same relational space. Node representations are updated using our edge-aware graph transformer followed by attention-driven propagation, and a final graph-level embedding for benign-malignant classification. Experiments on four public benchmarks demonstrate that explicit region-level relational modeling and graph-native multimodal fusion yield consistent gains over the state-of-the-art. Consequently, we establish a new graph-centric perspective in which CNN features are modeled as relational nodes and improved through contextual integration, yielding more expressive and robust classifications.
We present a superpixel-based strategy for segmenting skin lesion on dermoscopic images. The segmentation is carried out by over-segmenting the original image using the SLIC algorithm, and then merge the resulting superpixels into two regions: healthy skin and lesion. The mean RGB color of each superpixel was used as merging criterion. The presented method is capable of dealing with segmentation problems commonly found in dermoscopic images such as hair removal, oil bubbles, changes in illumination, and reflections images without any additional steps. The method was evaluated on the PH2 and IS…
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
Beihang University（北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Salesforce AI Research（赛富时人工智能研究院）；Didi Chuxing（滴滴出行）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Lehigh University（理海大学）
The accurate detection of lesion attributes is meaningful for both the computeraid diagnosis system and dermatologists decisions. However, unlike lesion segmentation and melenoma classification, there are few deep learning methods and literatures focusing on this task. Currently, the lesion attribute detection still remains challenging due to the extremely unbalanced class distribution and insufficient samples, as well as large intraclass and low interclass variations. To solve these problems, we propose a deep learning framework named superpixel attention network (SANet). Firstly, we segment …
National Engineering School of Sousse（苏塞国家工程学院）；University of Dundee（邓迪大学）
We introduce a dynamic multiscale tree (DMT) architecture that learns how to leverage the strengths of different existing classifiers for supervised multi-label image segmentation. Unlike previous works that simply aggregate or cascade classifiers for addressing image segmentation and labeling tasks, we propose to embed strong classifiers into a tree structure that allows bi-directional flow of information between its classifier nodes to gradually improve their performances. Our DMT is a generic classification model that inherently embeds different cascades of classifiers while enhancing learn…
GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions
School of Computer Science, Institute for Research in Fundamental Sciences (IPM)（计算机科学学院，基础科学研究所（IPM））；Department of Physics, Shahid Beheshti University（物理系，肖德·贝赫谢什提大学）；Faculty of Science, University of Tehran（科学学院，德黑兰大学）；Fatemeh Zahra Hospital, Alborz University of Medical Sciences（法特梅·扎拉医院，阿尔bor兹医学科学大学）；Department of Electrical and Computer Engineering, Sungkyunkwan University（电气与计算机工程系，成均馆大学）；Department of Computer Science and Engineering, Shahid Beheshti University（计算机科学与工程系，肖德·贝赫谢什提大学）；Autoimmune Bullous Diseases Research Center, Razi Hospital, Tehran University of Medical Sciences（自身免疫性水疱病研究中心，拉齐医院，德黑兰医学科学大学）
Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images
Learning Superpixel Ensemble and Hierarchy Graphs for Melanoma Detection
Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University（开罗大学工程学院生物医学工程与系统系）；School of Information Technology, New Giza University（新吉萨大学信息技术学院）；Department of Mathematics, Faculty of Science, Cairo University（开罗大学理学院数学系）
S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation
School of Electronic and Electrical Engineering, Shanghai University of Engineering Technology（上海工程技术大学电子与电气工程学院）；Shanghai Electric Power Hospital（上海电力医院）；Shanghai IDEAL INFORMATION Industry Co., LTD（上海理想信息产业有限公司）；Harvard University（哈佛大学）
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；The University of Queensland（昆士兰大学）；University of Athens Medical School（雅典大学医学院）；IBM Research（IBM研究院）；Universitat de Barcelona（巴塞罗那大学）；IDIBAPS（IDIBAPS（生物医学研究所））；Melanoma Institute Australia（澳大利亚黑色素瘤研究所）；Emory University School of Medicine（埃默里大学医学院）；Medical University of Vienna（维也纳医科大学）；Mayo Clinic（梅奥诊所）；SUNY Downstate Medical School（纽约州立大学唐斯泰特医学院）；Stony Brook Medical School（石溪大学医学院）；Rabin Medical Center（拉宾医疗中心）；Weill Cornell Medical College（威尔康奈尔医学院）
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier …
The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection
Computer Vision and Robotics Research Institute, University of Girona（赫罗纳大学计算机视觉与机器人研究所）；Hospital Clínic Barcelona, Universitat de Barcelona（巴塞罗那大学巴塞罗那临床医院）；Frazer Institute, The University of Queensland（昆士兰大学弗雷泽研究所）；University of Trieste（的里雅斯特大学）；ISAHIT；Canfield Scientific, Inc.（坎菲尔德科学公司）；HUN-REN Institute for Computer Science and Control（HUN-REN计算机科学与控制研究所）
