Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale

Research Content

1. Establishment of Ultrasound and Magnetic Resonance Imaging-Pathology-Clinical Dataset: This project aims to include medical records diagnosed through ultrasound and magnetic resonance imaging. Combining results from cytopathology or tissue pathology, the project will collect ultrasound images of the bladder, magnetic resonance images of the bladder, pathology images, and clinically relevant follow-up data. Tumor tissue specimens will be collected for immunohistochemical fluorescence staining. Participants with indistinct imaging findings or those with other primary malignant tumors will be excluded. The goal is to establish an ultrasound and magnetic resonance imaging-pathology-clinical dataset.
2. Establishment of an Artificial Intelligence System for Precise Diagnosis of Bladder Cancer Staging and Prognosis based on Ultrasound Imaging: Collecting clinical baseline information, surgical pathology, and ultrasound images of enrolled subjects. The ultrasound images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, and attention mechanisms for tissue segmentation and feature extraction, a deep learning model based on convolutional neural networks will be established for precise diagnosis of bladder cancer staging and prognosis:

Construction of a mathematical model for staging bladder cancer using ultrasound contrast: Using convolutional neural networks for deep learning to build a mathematical model for staging bladder cancer. Developing an artificial intelligence diagnostic system for ultrasound contrast images based on deep learning and mathematical models to determine whether bladder cancer has muscle invasion.

Construction of a mathematical model to discriminate prognosis features of bladder cancer using ultrasound imaging: Automatically delineating target areas and extracting ultrasound image features of bladder cancer lesions using new artificial intelligence technology - convolutional neural networks to build a model for evaluating the prognosis of bladder cancer lesions and achieving accurate prognosis diagnosis. 3. Establishment of an Artificial Intelligence System for the Joint Diagnosis of Bladder Cancer and Staging based on Ultrasound and Magnetic Resonance Imaging with Pathology: Based on bladder ultrasound, magnetic resonance imaging, and pathology image data, the baseline information of study subjects will be digitized. Ultrasound images, magnetic resonance images, and pathology images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, and attention mechanisms for segmentation and feature extraction of bladder ultrasound images, magnetic resonance images, and pathology images, a deep learning model based on convolutional neural networks will be established for the precise diagnosis of bladder cancer staging and prognosis:

Construction of a mathematical model for joint pathology-based staging of bladder cancer using ultrasound and magnetic resonance imaging: Using convolutional neural networks for deep learning to build a mathematical model for staging bladder cancer. Based on the mathematical model, continuously optimizing algorithms, developing multi-omics, multidimensional artificial intelligence diagnostic systems based on ultrasound images, magnetic resonance images, pathology images, and clinical features, achieving accurate diagnosis of bladder cancer staging and prognosis prediction models.