In precision medicine, the diagnosis and prognosis of colorectal cancer (CRC) has always been a clinical focus, and subjective evaluation of histological slides by highly trained pathologists remains the gold standard in this field. With the innovation of deep learning in image analysis, convolutional neural networks (CNNs) can extract quantitative information from HE pathological images, and We found a certain correlation between this information and the progression of colorectal cancer. In this study, we first used a CNN model to classify HE pathological images. The CNN model was trained and validated using patches of 86 (from the NCT biobank and the UMM pathology archive) and 25 (from the DACHS study in the NCT biobank) colorectal HE pathological images, respectively. With this tool, we performed automated tissue decomposition of representative multitissue HE images from the The Cancer Genome Atlas (TCGA) cohort. Based on the output neuron activations in the CNN, we calculated the tumor-stroma-ratio (TSR). This score was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model. Finally, we validated these findings in an independent CRC dataset. Again, the score was an independent prognostic factor for OS. This study, deep learning methods can decompose complex tissue pathological images and extract prognostic factors from HE pathological images.
Alzheimer's disease (AD) is a common neurodegenerative disease, whose early diagnosis is crucial for disease control and treatment. This study aims to explore the use of ensemble learning to analyze data from AD patients using multimodal inputs, including MRI image features extracted by convolutional neural networks (CNN), age, gender, APOE status and clinical functional scales. Firstly, we preprocess and extract the key image information features related to AD from MRI images. We then used multiple machine learning (ML) methods to build different classifiers, and combined these different classifiers by voting to obtain more accurate prediction results. Our method has been validated on a large AD patient database.The results demonstrated that the analysis of multimodal data can significantly improve the diagnostic accuracy of AD compared to single-mode data, while ensemble learning further improves the stability of the model.
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