MedicalNet
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Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relativ...
I'd like to share the code of classification with modification of train.py and datasets/brains18.py The code is based on JasperHG90's commented at #58 To deal with the issue of Classification,...
Hi, I want to use resnet_50.pth pre-trained encoder to extract 3D feature maps from medical images. Is the following method correct? It seems strange that the parameters of width, height,...
Hi, thank you for sharing your excellent work! I wanna test your pre-trained lung segmentation model ([this release](https://github.com/Tencent/MedicalNet#update20190730)) with my CT data, and I encountered the following error when running...
Hi, I am a bit new in the field. I just wanted to ask how can I load the pretrained model. Thanks
Dataset: https://github.com/rsingla92/kidneyUS/ I want to understand how MedicalNet can detect CKDs in Kidney Ultrasound Scans. Any guidance would be much appreciated.
Bumps [scipy](https://github.com/scipy/scipy) from 1.1.0 to 1.10.0. Release notes Sourced from scipy's releases. SciPy 1.10.0 Release Notes SciPy 1.10.0 is the culmination of 6 months of hard work. It contains many...
could you please tell me how many graphic memories are required? can i use my 6gb memories to run this project
Thank you for your work. I wonder why lr*100 and i have found the rate of convergence could get higher with original lr. Thanks a lot. { 'params': parameters['base_parameters'], 'lr':...
hello, I found this 2.Download data & pre-trained models ([Google Drive](https://drive.google.com/file/d/13tnSvXY7oDIEloNFiGTsjUIYfS3g3BfG/view?usp=sharing) or [Tencent Weiyun](https://share.weiyun.com/55sZyIx)) on your site, but maybe I misread it on which dataset these networks are trained. Maybe...
Do you add noise on the background of input images in function '__itensity_normalize_one_volume__'?
``` def __itensity_normalize_one_volume__(self, volume): pixels = volume[volume > 0] mean = pixels.mean() std = pixels.std() out = (volume - mean)/std out_random = np.random.normal(0, 1, size = volume.shape) out[volume == 0]...