标题： 深度学习用于胶质母细胞瘤形态病理特征识别：BraTS-病理学挑战解决方案 显示英文标题

标题： Deep Learning for Glioblastoma Morpho-pathological Features Identification: A BraTS-Pathology Challenge Solution

摘要： 胶质母细胞瘤是一种具有多种分子和病理特征的高度侵袭性脑肿瘤，由于其异质性，诊断具有挑战性。 准确的诊断和对这种异质性的评估对于选择正确的治疗方案和改善患者预后至关重要。 传统方法依赖于在组织样本中识别特定特征，但深度学习为改进胶质母细胞瘤的诊断提供了有前景的方法。 在本文中，我们介绍了我们对BraTS-Path Challenge 2024的方法。 我们利用一个预训练模型并在BraTS-Path训练数据集上对其进行微调。 我们的模型在具有挑战性的BraTS-Path验证集上表现不佳，这通过Synapse在线平台进行了严格评估。 该模型的准确率为0.392229，召回率为0.392229，F1分数为 0.392229，表明其在目标条件下正确识别实例方面具有稳定的能力。 值得注意的是，我们的模型表现出完美的特异性0.898704，显示出正确分类阴性病例的非凡能力。 此外，计算出的马修斯相关系数（MCC）为0.255267，以表示预测值和实际值之间的有限正相关，并突出显示我们模型的整体预测能力。 我们的解决方案在测试阶段也获得了第二名。 显示英文摘要

摘要： Glioblastoma, a highly aggressive brain tumor with diverse molecular and pathological features, poses a diagnostic challenge due to its heterogeneity. Accurate diagnosis and assessment of this heterogeneity are essential for choosing the right treatment and improving patient outcomes. Traditional methods rely on identifying specific features in tissue samples, but deep learning offers a promising approach for improved glioblastoma diagnosis. In this paper, we present our approach to the BraTS-Path Challenge 2024. We leverage a pre-trained model and fine-tune it on the BraTS-Path training dataset. Our model demonstrates poor performance on the challenging BraTS-Path validation set, as rigorously assessed by the Synapse online platform. The model achieves an accuracy of 0.392229, a recall of 0.392229, and a F1-score of 0.392229, indicating a consistent ability to correctly identify instances under the target condition. Notably, our model exhibits perfect specificity of 0.898704, showing an exceptional capacity to correctly classify negative cases. Moreover, a Matthews Correlation Coefficient (MCC) of 0.255267 is calculated, to signify a limited positive correlation between predicted and actual values and highlight our model's overall predictive power. Our solution also achieves the second place during the testing phase.