from fastai.vision.all import *
from fastai.medical.imaging import *
import torchvision
import torchvision.transforms as t
pneumothorax_source = untar_data(URLs.SIIM_SMALL)
items = get_dicom_files(pneumothorax_source/f"train/")
df = pd.read_csv(pneumothorax_source/f"labels.csv")
df.head()
file label
0 train/No Pneumothorax/000000.dcm No Pneumothorax
1 train/Pneumothorax/000001.dcm Pneumothorax
2 train/No Pneumothorax/000002.dcm No Pneumothorax
3 train/Pneumothorax/000003.dcm Pneumothorax
4 train/Pneumothorax/000004.dcm Pneumothorax
df_pneumo = df[df['label']=='Pneumothorax']
pneumothorax = DataBlock(blocks=(ImageBlock(cls=PILDicom), MaskBlock(codes=['bg','pneumo'])),
                   get_x=lambda x:pneumothorax_source/f"{x[0]}",
                   get_y=lambda x:'example/siim/'+Path(x[0]).stem+'.png',
                   batch_tfms=aug_transforms(do_flip=False,p_affine=0,p_lighting=0,size=224))

dls = pneumothorax.dataloaders(df_pneumo.values, num_workers=0, bs=16)
dls.show_batch(max_n=16, vmin=0)
learn = unet_learner(dls, resnet34)
learn.fine_tune(1)
epoch train_loss valid_loss time
0 1.382017 0.309487 01:04
epoch train_loss valid_loss time
0 0.167802 0.111701 00:51
# this is skipped when testing, instead a single epoch is done (not sufficient for overfitting)
learn = unet_learner(dls, resnet34)
learn.fine_tune(8)
epoch train_loss valid_loss time
0 2.200016 0.416453 01:05
epoch train_loss valid_loss time
0 0.195540 0.105748 00:51
1 0.148728 0.141623 00:51
2 0.126741 0.176495 00:54
3 0.116757 0.106115 00:57
4 0.106740 0.093054 02:16
5 0.098731 0.140762 01:49
6 0.092059 0.120493 02:27
7 0.086470 0.116142 01:48
learn.show_results(ds_idx=0,max_n=9,vmin=0)
preds = learn.get_preds(ds_idx=0)
plt.imshow(preds[1][2])
<matplotlib.image.AxesImage at 0x7f900a0926d0>
preds[0].argmax(dim=1).sum()
#plt.imshow(preds[0].argmax(dim=1)[0])
TensorBase(0)
learn.fine_tune(8)
epoch train_loss valid_loss time
0 0.061920 2.473048 01:03
epoch train_loss valid_loss time
0 0.314660 0.080977 01:54
1 0.208264 0.088037 00:55
2 0.160613 0.078349 00:54
3 0.134872 0.086172 00:52
4 0.113790 0.082046 00:52
5 0.100933 0.077185 00:53
6 0.090635 0.089535 01:10
7 0.083492 0.089540 01:10
learn.recorder.plot_loss()
preds = learn.get_preds(ds_idx=0)
preds[0].argmax(dim=1).sum()
TensorBase(0)
fig, axs = plt.subplots(5,2,figsize=(8,16))
for i in range(5):
    axs[i,0].imshow(preds[1][i])
    axs[i,1].imshow(preds[0][i][1])
    print(preds[0][i][1].max())
TensorBase(0.3102)
TensorBase(0.3205)
TensorBase(0.3076)
TensorBase(0.2694)
TensorBase(0.0728)
learn.fit_one_cycle(8)
epoch train_loss valid_loss time
0 0.043738 0.085497 01:06
1 0.041399 0.126955 00:53
2 0.039557 0.166106 00:52
3 0.038748 0.160928 00:55
4 0.036368 0.123756 00:52
5 0.034367 0.111612 00:52
6 0.032606 0.130679 00:52
7 0.030831 0.122198 00:53
preds = learn.get_preds(ds_idx=0)
preds[0].argmax(dim=1).sum()
TensorBase(14126)
learn.fit_one_cycle(20)
epoch train_loss valid_loss time
0 0.023082 0.114249 00:56
1 0.022736 0.110707 00:53
2 0.021745 0.209588 00:52
3 0.022693 0.105819 01:18
4 0.024324 0.151829 00:49
5 0.024436 0.093544 00:49
6 0.024203 0.123490 00:48
7 0.023524 0.166477 00:50
8 0.022349 0.262088 00:55
9 0.021310 0.254958 01:41
10 0.020904 0.199529 02:09
11 0.020301 0.199600 00:58
12 0.019917 0.186603 01:07
13 0.019124 0.135860 00:52
14 0.018472 0.152205 00:49
15 0.017754 0.178598 00:52
16 0.017116 0.187399 00:56
17 0.016518 0.185505 00:56
18 0.015890 0.184788 00:57
19 0.015337 0.181746 00:57
preds = learn.get_preds(ds_idx=0)
preds[0].argmax(dim=1).sum()
TensorBase(41177)
fig, axs = plt.subplots(5,3,figsize=(12,16))
for i in range(5):
    axs[i,0].imshow(preds[1][i], interpolation="nearest")
    axs[i,1].imshow(preds[0][i][1])
    axs[i,2].imshow(preds[0].argmax(dim=1)[i], interpolation="nearest")
learn.save('pneumothorx')
Path('models/pneumothorx.pth')

We managed to overfit

learn.load('pneumothorx')
<fastai.learner.Learner at 0x7f9022ba4c70>
from misas.core import *
from fastai.vision.all import * #open_mask, Image, ImageSegment
import pydicom
def read_dcm(file):
    ds = pydicom.dcmread(file)
    img = Tensor(ds.pixel_array.astype(np.int16))
    img = img/img.max()
    transform = t.ToPILImage()
    img = transform (torch.stack([img, img, img]))
    return img
    
learn.predict(learn.dls.train_ds.items[1])[0].show(vmin=0)
<AxesSubplot:>
fname = df_pneumo.iloc[3]['file']
img = lambda: read_dcm(pneumothorax_source/fname)
trueMask = lambda: Image.open('example/siim/'+Path(fname).stem+'.png') #open_mask('example/siim/'+Path(fname).stem+'.png')
dcm = img()
dcm
#to_image(dcm.data)