School of Computer Science, Hunan First Normal University, Changsha, 410205, China.
Department of Dermatology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China; Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China; Department of Geriatrics, Shenzhen People's Hospital, (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China.
Comput Biol Med. 2024 Nov;182:109152. doi: 10.1016/j.compbiomed.2024.109152. Epub 2024 Sep 19.
Pilomatricoma, a benign childhood skin tumor, presents diagnostic challenges due to its manifestation variations and requires surgical excision upon histological confirmation of its characteristic cellular features. Recent artificial intelligence (AI) advancements in pathology promise enhanced diagnostic accuracy and treatment approaches for this neoplasm.
We employed a multiscale transfer learning model, initiating the training process at high resolutions and adapting to broader scales. For evaluation purposes, we applied metrics such as accuracy, precision, recall, the F1 score, and the area under the receiver operating characteristic curve (AUROC) to measure the performance of the model, with the statistical significance of the results assessed via two-sided P tests. Our novel approach also included a retrosynthetic saliency mapping technique to achieve enhanced lesion visualization in whole-slide images (WSIs), supporting pathologists' diagnostic processes.
Our model effectively navigated the challenges of global-scale classification, achieving a high validation accuracy of up to 0.973 despite some initial fluctuations. This method displayed excellent accuracy in terms of identifying basaloid and ghost cells, especially at lower scales, with slight variability in its ghost cell accuracy and more noticeable changes in the 'Other' category at higher scales. The consistent performance attained for basaloid cells was clear across all scales, whereas areas for improvement were identified in the 'Other' category. The model also excelled at generating detailed and interpretable saliency maps for lesion visualization purposes, thereby enhancing its value in digital pathology diagnostics.
Our pilomatricoma study demonstrates the efficacy of a deep learning-based histopathological diagnosis model, as validated by its high performance across various scales, and it is enhanced by an innovative retrosynthetic approach for saliency mapping.
毛母质瘤是一种良性儿童皮肤肿瘤，由于其表现形式多样，诊断具有挑战性，需要在组织学确认其特征性细胞特征后进行手术切除。最近病理学领域的人工智能 (AI) 进展有望提高这种肿瘤的诊断准确性和治疗方法。
我们采用了一种多尺度迁移学习模型，从高分辨率开始训练过程，并适应更广泛的尺度。为了评估目的，我们应用了准确性、精度、召回率、F1 分数和接收器操作特征曲线 (AUROC) 下面积等指标来衡量模型的性能，通过双侧 P 检验评估结果的统计显著性。我们的新方法还包括一种回溯显着性映射技术，以实现全幻灯片图像 (WSI) 中病变的增强可视化，支持病理学家的诊断过程。
我们的模型有效地解决了全局尺度分类的挑战，尽管最初存在一些波动，但验证准确率高达 0.973。该方法在识别基底样细胞和幽灵细胞方面表现出出色的准确性，尤其是在较低的尺度下，幽灵细胞的准确性略有变化，而在较高的尺度下，“其他”类别的变化更为明显。基底样细胞的一致性能在所有尺度上都很明显，而“其他”类别则需要改进。该模型还擅长为病变可视化生成详细且可解释的显着性图，从而提高其在数字病理学诊断中的价值。
我们的毛母质瘤研究证明了基于深度学习的组织病理学诊断模型的有效性，其在各种尺度上的高性能得到了验证，并且通过创新的回溯方法进行显着性映射得到了增强。
