A5067515382- Cervical Cancer and HPV Research
- AI in cancer detection
- Medical Imaging and Analysis
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Image and Signal Denoising Methods
- Image Processing Techniques and Applications
- Multimodal Machine Learning Applications
- Advanced Image Processing Techniques
Tianjin University
2022
Tianjin Medical University
2022
Colposcopy is an important method in the diagnosis of cervical lesions. However, experienced colposcopists are lacking at present, and training cycle long. Therefore, artificial intelligence-based colposcopy-assisted examination has great prospects. In this paper, a lesion segmentation model (CLS-Model) was proposed for region from colposcopic post-acetic-acid images accurate results could provide good foundation further research on classification selection biopsy site.First, improved Faster...
Objective: Cervical cancer is one of the two biggest killers women and early detection cervical precancerous lesions can effectively improve survival rate patients. Manual diagnosis by combining colposcopic images clinical examination results main method at present. Developing an intelligent algorithm based on artificial intelligence inevitable trend to solve objectification quality efficiency diagnosis.Approach: A multimodal fusion convolutional neural network (CMF-CNN) was proposed for...
Background: Bone microstructure is important for evaluating bone strength and requires the support of high-resolution (HR) imaging equipment. Computed tomography (CT) widely used medical imaging, but spatial resolution not sufficient microstructure. Micro-CT scan data gold standard human or animal experiment. However, has more ionizing radiation longer scanning time while providing high-quality imaging. It makes sense to reconstruct HR images with less radiation. Image super-resolution (SR)...
Colposcopy is one of the common methods cervical cancer screening. The type transformation zone considered important factors for grading colposcopic findings and choosing treatment.This study aims to develop a deep learning-based method automatic classification from colposcopy images.We proposed multiscale feature fusion network classify zone, which can extract features images fuse them at multiple scales. Cervical regions were first detected original then fed into our network.The results on...
Colposcopy is a cervical cancer screening method recommended by the World Health Organization. The main task of colposcopy to detect high-grade squamous intraepithelial lesions (HSIL) and early (collectively referred as HSIL+). However, boundaries these are blurry, size varies greatly, which poses huge challenge computer aided diagnosis (CAD). In this paper, novel deep learning-based for HSIL+ detection proposed, network (HSILDNet), uses bidirectional feature pyramid (BiFPN) cascade head...