Methodology for Creating a Clinically Verified Dermoscopic Image Dataset
- URL: http://arxiv.org/abs/2605.25168v1
- Date: Sun, 24 May 2026 16:56:47 GMT
- Title: Methodology for Creating a Clinically Verified Dermoscopic Image Dataset
- Abstract summary: This study presents a methodology for constructing a clinically verified dataset of dermatoscopic images for medical informatics research.<n>International collections were primarily created under conditions that differ substantially from routine Russian outpatient practice and mobile dermatoscopy.<n>Using this methodology, a dataset of 1,026 unique dermatoscopic images from 443 patients was collected between June 2025 and May 2026.<n>The dataset includes nine nosological categories; all 39 malignant lesions were histologically verified.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This study presents a methodology for constructing a clinically verified dataset of dermatoscopic images for medical informatics research. The relevance of the work is driven by the fact that the performance of automated diagnostic support systems depends not only on the volume of images, but also on the reproducibility of the image acquisition procedure, the completeness of structured metadata, and the reliability of diagnostic labels. International collections were primarily created under conditions that differ substantially from routine Russian outpatient practice and mobile dermatoscopy. The proposed methodology integrates three interconnected components: (1) a standard operating procedure (SOP) for acquiring images via mobile dermatoscopy, (2) an information model comprising 16 structured metadata fields organized into six clinically oriented blocks in ISIC-compatible notation, and (3) a multi-stage expert verification of diagnostic labels (initial clinical annotation, consensus review by three specialists, and histological confirmation of all malignant neoplasms). Using this methodology, a dataset of 1,026 unique dermatoscopic images from 443 patients was collected between June 2025 and May 2026. From 1,044 initial records, 18 duplicates were excluded. The dataset includes nine nosological categories; all 39 malignant lesions (18 melanomas, 15 basal cell carcinomas, and 6 squamous cell carcinomas) were histologically verified. Patient age ranged from 2 to 90 years (median 38), with 279 females (63%) and 164 males (37%). Each image is accompanied by expert-annotated dermatoscopic structures and an explicit verification_stage field indicating the level of diagnostic confirmation. The resulting dataset serves as a pilot clinically verified resource suitable for independent model evaluation, domain shift analysis, interpretability studies, and further expansion.
Related papers
- A report-grounded vision-language foundation model for colonoscopy from 280000 routine reports [58.14965296904734]
EndoCLIP is a vision-language foundation model trained on 125,756 lesion-level image-text pairs.<n>It outperforms general-purpose and biomedical vision-language encoders in both zero-shot and linear-probe settings.
arXiv Detail & Related papers (2026-07-30T16:24:36Z) - Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images [2.4629531282150876]
We propose a semantic segmentation-based framework for image-level diagnosis.<n>Image-level predictions are obtained by assigning the class of the dominant pixel-level label in the segmentation output.<n>Five-fold cross-validation yielded balanced accuracy of 0.975 (HCC), 0.950 (CCA), and 1.000 (CMA)
arXiv Detail & Related papers (2026-07-03T12:12:56Z) - A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
arXiv Detail & Related papers (2025-12-15T10:22:43Z) - DeepGI: Explainable Deep Learning for Gastrointestinal Image Classification [0.0]
The study confronts common endoscopic challenges such as variable lighting, fluctuating camera angles, and frequent imaging artifacts.<n>The best performing models, VGG16 and MobileNetV2, each achieved a test accuracy of 96.5%.<n>The approach includes explainable AI via Grad-CAM visualization, enabling identification of image regions most influential to model predictions.
arXiv Detail & Related papers (2025-11-26T22:35:57Z) - Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using
Adversarial Training [0.8287206589886881]
We train 21 convolutional neural network (CNN) models on a diverse set of 33,000+ chest X-ray (CXR) images to classify between healthy, COVID-19, and non-COVID-19 pneumonia CXRs.
Our resulting models achieved a 3-way classification accuracy, recall, and precision of up to 97.03%, 97.97%, and 99.95%, respectively.
arXiv Detail & Related papers (2023-11-23T23:40:01Z) - A Transformer-based representation-learning model with unified
processing of multimodal input for clinical diagnostics [63.106382317917344]
We report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner.
The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases.
arXiv Detail & Related papers (2023-06-01T16:23:47Z) - Spectral Machine Learning for Pancreatic Mass Imaging Classification [2.841795278340179]
spectral machine learning method is used in screening for pancreatic mass using CT imaging.
A test accuracy of 94.6 percents was achieved in the out-of-sample diagnosis classification.
arXiv Detail & Related papers (2021-05-03T10:17:32Z) - Malignancy Prediction and Lesion Identification from Clinical
Dermatological Images [65.1629311281062]
We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images.
We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
arXiv Detail & Related papers (2021-04-02T20:52:05Z) - A Patient-Centric Dataset of Images and Metadata for Identifying
Melanomas Using Clinical Context [39.10946113351587]
The 2020 SIIM-ISIC Melanoma Classification challenge dataset was constructed to address the discrepancy between prior challenges and clinical practice.
The dataset represents 2,056 patients from three continents with an average of 16 lesions per patient.
arXiv Detail & Related papers (2020-08-07T20:22:23Z) - Co-Heterogeneous and Adaptive Segmentation from Multi-Source and
Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion
Segmentation [48.504790189796836]
We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe)
We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling.
CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
arXiv Detail & Related papers (2020-05-27T06:58:39Z) - Semi-supervised Medical Image Classification with Relation-driven
Self-ensembling Model [71.80319052891817]
We present a relation-driven semi-supervised framework for medical image classification.
It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations.
Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
arXiv Detail & Related papers (2020-05-15T06:57:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.