Deep-Learning-with-PyTorch

Updating the training script for segmentation
395
Our approach will cheat a little by adding together various images and masks to get
data in the range 0 to 2, and then multiplying the entire array by 0.5 to get it back into
the right range.
Listing 13.29
training.py:346, .logImages
CT intensity is assigned to all RGB channels
to provide a grayscale base image.
ct_t[:-1,:,:] /= 2000
ct_t[:-1,:,:] += 0.5
ctSlice_a = ct_t[dl.dataset.contextSlices_count].numpy()
image_a = np.zeros((512, 512, 3), dtype=np.float32)
image_a[:,:,:] = ctSlice_a.reshape((512,512,1))
image_a[:,:,0] += prediction_a & (1 - label_a)
image_a[:,:,0] += (1 - prediction_a) & label_a
image_a[:,:,1] += ((1 - prediction_a) & label_a) * 0.5
False positives are flagged as
red and overlaid on the image.
False negatives
are orange.
image_a[:,:,1] += prediction_a & label_a
image_a *= 0.5
image_a.clip(0, 1, image_a)
True positives
are green.
Our goal is to have a grayscale CT at half intensity, overlaid with predicted-nodule (or,
more correctly, nodule-candidate) pixels in various colors. We’re going to use red for
all pixels that are incorrect (false positives and false negatives). This will mostly be
false positives, which we don’t care about too much (since we’re focused on recall).
1 - label_a inverts the label, and that multiplied by the prediction_a gives us only
the predicted pixels that aren’t in a candidate nodule. False negatives get a halfstrength
mask added to green, which means they will show up as orange (1.0 red and
0.5 green renders as orange in RGB). Every correctly predicted pixel inside a nodule
is set to green; since we got those pixels right, no red will be added, and so they will
render as pure green.
After that, we renormalize our data to the 0…1 range and clamp it (in case we start
displaying augmented data here, which would cause speckles when the noise was outside
our expected CT range). All that remains is to save the data to TensorBoard.
Listing 13.30
training.py:361, .logImages
writer = getattr(self, mode_str + '_writer')
writer.add_image(
f'{mode_str}/{series_ndx}_prediction_{slice_ndx}',
image_a,
self.totalTrainingSamples_count,
dataformats='HWC',
)