Title: Deep Learning Framework for Early Detection of Pancreatic Cancer Using Multi-Modal Medical Imaging Analysis Show Chinese title

Title: 用于利用多模态医学影像分析进行胰腺癌早期检测的深度学习框架

Abstract: Pacreatic ductal adenocarcinoma (PDAC) remains one of the most lethal forms of cancer, with a five-year survival rate below 10% primarily due to late detection. This research develops and validates a deep learning framework for early PDAC detection through analysis of dual-modality imaging: autofluorescence and second harmonic generation (SHG). We analyzed 40 unique patient samples to create a specialized neural network capable of distinguishing between normal, fibrotic, and cancerous tissue. Our methodology evaluated six distinct deep learning architectures, comparing traditional Convolutional Neural Networks (CNNs) with modern Vision Transformers (ViTs). Through systematic experimentation, we identified and overcome significant challenges in medical image analysis, including limited dataset size and class imbalance. The final optimized framework, based on a modified ResNet architecture with frozen pre-trained layers and class-weighted training, achieved over 90% accuracy in cancer detection. This represents a significant improvement over current manual analysis methods an demonstrates potential for clinical deployment. This work establishes a robust pipeline for automated PDAC detection that can augment pathologists' capabilities while providing a foundation for future expansion to other cancer types. The developed methodology also offers valuable insights for applying deep learning to limited-size medical imaging datasets, a common challenge in clinical applications. Show Chinese abstract

Abstract: 胰腺导管腺癌（PDAC）仍然是最致命的癌症形式之一，五年生存率低于10%，主要是由于发现较晚。 这项研究开发并验证了一个深度学习框架，通过分析双模态成像：自体荧光和二次谐波生成（SHG），以实现早期PDAC检测。 我们分析了40个独特的患者样本，创建了一个专门的神经网络，能够区分正常组织、纤维化组织和癌变组织。 我们的方法评估了六种不同的深度学习架构，将传统的卷积神经网络（CNNs）与现代的视觉变换器（ViTs）进行了比较。 通过系统实验，我们识别并克服了医学图像分析中的重大挑战，包括数据集规模有限和类别不平衡。 最终优化的框架基于修改后的ResNet架构，具有冻结的预训练层和类别加权训练，在癌症检测中达到了90%以上的准确率。 这相对于当前的人工分析方法有了显著改进，并展示了临床部署的潜力。 这项工作建立了一个强大的自动化PDAC检测流程，可以增强病理学家的能力，同时为未来扩展到其他癌症类型奠定了基础。 所开发的方法还为将深度学习应用于小规模医学影像数据集提供了有价值的见解，这是临床应用中的常见挑战。