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.
背景：黑色素瘤的早期检测对改善患者预后至关重要，因为随着疾病进展，生存率会急剧下降。尽管深度学习在皮肤病变分析方面已取得显著成果，但若干挑战限制了其在临床实践中的有效性。关键问题之一是训练数据中缺乏黑色素瘤分期分布信息，这引发了对这些模型准确检测早期黑色素瘤能力的担忧。此外，包含黑色素瘤分期和肿瘤厚度详细信息的公开数据集仍然稀缺，限制了研究人员开发专门针对早期诊断的方法并进行基准测试。另一个主要局限是缺乏跨数据集评估。大多数深度学习模型仅在其训练所用的相同数据集上进行测试，因此无法评估其在未见数据上的泛化能力，这降低了其在真实临床环境中的可靠性。方法：我们引入了一个早期黑色素瘤基准数据集以解决这些问题，该数据集包含根据Breslow厚度按T分期标注的图像。结果：我们在该数据集上评估了多种先进深度学习模型，发现其性能相较于在ISIC挑战数据集上的结果显著下降。结论：这一发现凸显了模型在检测早期黑色素瘤方面的能力局限。本研究通过提供针对T分期的分析资源并支持跨数据集评估，旨在推动自动化黑色素瘤诊断系统的开发与临床应用。
肿瘤(癌症)患者之家