# Early-Stage Melanoma Benchmark Dataset

## TL;DR

A new dataset called EMB helps improve early melanoma detection by providing labeled images for deep learning models.

## Contribution

The EMB dataset introduces labeled images for early-stage melanoma detection, enabling T-category-specific analysis and cross-dataset benchmarking.

## Key findings

- State-of-the-art models show reduced performance on EMB compared to ISIC datasets, especially for in situ and thin melanomas.
- EMB enables T-category-specific analysis and cross-dataset benchmarking for early-stage melanoma detection.
- The dataset highlights the challenges in detecting early-stage melanomas using current deep learning models.

## Abstract

The Early-Stage Melanoma Benchmark (EMB) dataset was designed to support the development and evaluation of deep learning models for early melanoma detection. Existing datasets often lack information on melanoma stage or Breslow thickness, limiting researchers’ ability to accurately assess models in the early-stage melanoma detection task. EMB addresses this gap by providing over 1100 dermoscopic and clinical melanoma images labeled according to T-category in TNM classification. The dataset is curated from public sources and filtered to avoid overlap with ISIC training data. Several state-of-the-art models were evaluated on EMB, revealing a significant performance drop, particularly for in situ and thin melanomas, compared to results on standard ISIC datasets. This highlights the challenges of early-stage detection and the need for dedicated datasets. EMB enables cross-dataset benchmarking and T-category-specific analysis, offering a valuable resource for assessing clinical applicability and the robustness of automated melanoma diagnosis systems.

Background: The early detection of melanoma is crucial for improving patient outcomes, as survival rates decline dramatically with disease progression. Despite significant achievements in deep learning methods for skin lesion analysis, several challenges limit their effectiveness in clinical practice. One of the key issues is the lack of knowledge about the melanoma stage distribution in the training data, raising concerns about the ability of these models to detect early-stage melanoma accurately. Additionally, publicly available datasets that include detailed information on melanoma stage and tumor thickness remain scarce, restricting researchers from developing and benchmarking methods specifically tailored for early diagnosis. Another major limitation is the lack of cross-dataset evaluations. Most deep learning models are tested on the same dataset they were trained on, so they fail to assess their generalization ability when applied to unseen data. This reduces their reliability in real-world clinical settings. Methods: We introduce an early-stage melanoma benchmark dataset to address these issues, featuring images labeled according to T-category based on Breslow thickness. Results: We evaluated several state-of-the-art deep learning models on this dataset and observed a significant drop in performance compared to their results on the ISIC Challenge datasets. Conclusions: This finding highlights the models’ limited capability in detecting early-stage melanoma. This work seeks to advance the development and clinical applicability of automated melanoma diagnostic systems by providing a resource for T-category-specific analysis and supporting cross-dataset evaluation.