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import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.utils.model_zoo as model_zoo | |
from torch.autograd import Variable | |
pretrained_settings = { | |
'nasnetalarge': { | |
'imagenet': { | |
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth', |
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absl-py==0.6.1 | |
asn1crypto==0.24.0 | |
astor==0.7.1 | |
attrs==18.2.0 | |
backcall==0.1.0 | |
bcolz==1.2.1 | |
beautifulsoup4==4.6.3 | |
bleach==3.0.2 | |
bokeh==1.0.1 | |
boto==2.49.0 |
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scaled = scaled_px(patient6) | |
crop = scaled[280:400,300:450] | |
show_image(crop, cmap='bone'); |
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from fastai.vision.all import * | |
from fastai.medical.imaging import * | |
import matplotlib.pyplot as plt | |
plt.rcParams["figure.figsize"] = (7,7) | |
source = 'D:/Datasets/rsna_pulmonary' | |
files = get_dicom_files(source) | |
patient6 = files[6].dcmread() |
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