{"repo":"junqiangchen/PytorchDeepLearing","free":true,"listed":false,"github":"https://github.com/junqiangchen/PytorchDeepLearing","clone":"git clone https://github.com/junqiangchen/PytorchDeepLearing.git","description":"Meidcal Image Segmentation Pytorch Version","language":"Python","stars":340,"topics":["classification-model","pytorch-implementation","segmentation-models","unet-image-segmentation","vnet3d","flask","flask-application"],"license":null,"category":"api-integrations-sdks","readme_excerpt":"PytorchDeepLearing ImageSegment Model There are some Model NetWorks of 3D ImageSegment and 2D ImageSegment ImageSegment Nets There are Unet and Vnet Family all has 2d and 3d version. ImageSegment Loss Function There are some loss functions of 3D ImageSegment and 2D ImageSegment How to Use i have reimplemented the image segmentation loss functions with pytorch1.10.0 there are binary crossentropy,dice loss,focal loss sigmod etc all has 2d and 3d version. there are categorical loss functions of crossentropy,dice loss,focal loss etc all has 2d and 3d version. MS-SSIM loss and SSIM loss for calculating image similarity. centerline dice loss for vessel segmentation there are 9 type of segment metric,including dice,surface disatance,jaccard,VOE,RVD,FNR,FPR,ASSD,RMSD,MSD,etc. flask app.py is the demo example of the Flask Deep Learning Segmentation Model Service Deployment. SegmentwithSAM.py is 3D medical image interactive segmentation,support bbox,points,mask. SegmentwithSAM2.py is 3D medical image interactive segmentation,support bbox,points,mask. SegmentwithVISTA3D.py is 3D medical image interactive segmentation,only support points. SegmentwithnnInteractive.py is 3D medical image interactive segmentation,support bbox,points,mask,lasso. Contact https://github.com/junqiangchen email: 1207173174@qq.com WeChat Public number: 最新医学影像技术","default_branch":null,"files":null,"tree":[],"storefront":"/r/junqiangchen","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/junqiangchen/PytorchDeepLearing/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}