A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models
Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of unknown malignant potential (STUMP) or atypical Spitz tumor. Studies developing deep learning (DL) models to diagnose melanocytic tumors using whole slide imaging (WSI) are scarce, and few used ST for analysis, excluding STUMP. To address this gap, we introduce SOPHIE: the first ST dataset with WSIs, including labels as benign, malignant, and atypical tumors, along with the clinical information of each patient. Additionally, we explain two DL models implemented as validation examples using this database.
Code (0)
등록된 구현이 없습니다.
Tasks
Diagnosticwhole slide imagesSimilar Papers 제목 키워드 기반
Masked Autoencoder Joint Learning for Robust Spitzoid Tumor Classification
Accurate diagnosis of spitzoid tumors (ST) is critical to ensure a favorable prognosis and to avoid both under- and over-treatment. Epigenetic data, particularly DNA methylation, provide a valuable source of information …
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI
Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphologic…
Multiple Instance LearningPrognosisTransfer LearningSpitzoid Lesions Diagnosis based on GA feature selection and Random Forest
Spitzoid lesions broadly categorized into Spitz Nevus (SN), Atypical Spitz Tumors (AST), and Spitz Melanomas (SM). The accurate diagnosis of these lesions is one of the most challenges for dermapathologists; this is due …
feature selectionGeneral ClassificationSpecificityRegion of Interest Detection in Melanocytic Skin Tumor Whole Slide Images
Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational patho…
medical image detectionwhole slide imagesUnified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
The expanding ecosystem of pathology foundation models has produced powerful but fragmented tile-level representations, limiting their use in clinical tasks that require unified slide-level reasoning and interpretable li…
Representation LearningCancer Classification