Mahmood Tariq, Saba Tanzila, Al-Otaibi Shaha, Ayesha Noor, Almasoud Ahmed S
Artificial Intelligence and Data Analytics (AIDA) lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia.
Faculty of Information Sciences, University of Education, Vehari Campus, Vehari, Pakistan.
Microsc Res Tech. 2025 May;88(5):1335-1359. doi: 10.1002/jemt.24788. Epub 2025 Jan 2.
Microscopic imaging aids disease diagnosis by describing quantitative cell morphology and tissue size. However, the high spatial resolution of these images poses significant challenges for manual quantitative evaluation. This project proposes using computer-aided analysis methods to address these challenges, enabling rapid and precise clinical diagnosis, course analysis, and prognostic prediction. This research introduces advanced deep learning frameworks such as squeeze-and-excitation and dilated dense convolution blocks to tackle the complexities of quantifying small and intricate breast cancer tissues and meeting the real-time requirements of pathological image analysis. Our proposed framework integrates a dense convolutional network (DenseNet) with an attention mechanism, enhancing the capability for rapid and accurate clinical assessments. These multi-classification models facilitate the precise prediction and segmentation of breast lesions in microscopic images by leveraging lightweight multi-scale feature extraction, dynamic region attention, sub-region classification, and regional regularization loss functions. This research will employ transfer learning paradigms and data enhancement methods to enhance the models' learning further and prevent overfitting. We propose the fine-tuning employing pre-trained architectures such as VGGNet-19, ResNet152V2, EfficientNetV2-B1, and DenseNet-121, modifying the final pooling layer in each model's last block with an SPP layer and associated BN layer. The study uses labeled and unlabeled data for tissue microscopic image analysis, enhancing models' robust features and classification abilities. This method reduces the costs and time associated with traditional methods, alleviating the burden of data labeling in computational pathology. The goal is to provide a sophisticated, efficient quantitative pathological image analysis solution, improving clinical outcomes and advancing the computational field. The model, trained, validated, and tested on a microscope breast image dataset, achieved recognition accuracy of 99.6% for benign and malignant secondary classification and 99.4% for eight breast subtypes classification. Our proposed approach demonstrates substantial improvement compared to existing methods, which generally report lower accuracies for breast subtype classification ranging between 85% and 94%. This high level of accuracy underscores the potential of our approach to provide reliable diagnostic support, enhancing precision in clinical decision-making.
微观成像通过描述细胞定量形态和组织大小辅助疾病诊断。然而，这些图像的高空间分辨率给手动定量评估带来了重大挑战。本项目提出使用计算机辅助分析方法来应对这些挑战，实现快速准确的临床诊断、病程分析和预后预测。本研究引入了先进的深度学习框架，如挤压激励和空洞密集卷积块，以应对量化微小复杂乳腺癌组织的复杂性，并满足病理图像分析的实时需求。我们提出的框架将密集卷积网络（DenseNet）与注意力机制相结合，增强了快速准确临床评估的能力。这些多分类模型通过利用轻量级多尺度特征提取、动态区域注意力、子区域分类和区域正则化损失函数，促进了微观图像中乳腺病变的精确预测和分割。本研究将采用迁移学习范式和数据增强方法，进一步增强模型的学习能力并防止过拟合。我们建议使用预训练架构（如VGGNet-19、ResNet152V2、EfficientNetV2-B1和DenseNet-）进行微调，用SPP层和相关的BN层修改每个模型最后一个块中的最终池化层。该研究使用标记和未标记数据进行组织微观图像分析，增强模型的鲁棒特征和分类能力。这种方法降低了与传统方法相关的成本和时间，减轻了计算病理学中数据标记的负担。目标是提供一个复杂、高效的定量病理图像分析解决方案，改善临床结果并推动计算领域的发展。该模型在显微镜下乳腺图像数据集上进行训练、验证和测试，良性和恶性二级分类的识别准确率达到99.6%，八种乳腺亚型分类的准确率达到99.4%。与现有方法相比，我们提出的方法有显著改进，现有方法通常报告的乳腺亚型分类准确率较低，在85%至94%之间。这种高水平的准确率凸显了我们的方法提供可靠诊断支持的潜力，提高了临床决策的准确性。
