def model_selection(model_name, args):
# Load img, blobs and masks
imgs, blobs, paths = lidc.load(pts=True, set_name=args.ds_tr)
if args.ds_tr != args.ds_val:
_, blobs_val, _ = lidc.load(pts=True, set_name=args.ds_val)
else:
blobs_val = blobs
pred_blobs = detect.read_blobs('data/{}-lidc-pred-blobs.pkl'.format(
args.detector))
masks = np.load('data/aam-lidc-pred-masks.npy')
assert len(imgs) == len(masks) and len(pred_blobs) == len(masks)
# Load folds
folds = util.model_selection_folds(imgs)
# Create rois
rois = create_rois(imgs, masks, pred_blobs, args, real_blobs=blobs)
rois_val = create_rois(imgs, masks, pred_blobs, args, real_blobs=blobs_val)
# Set up CV
frocs = []
legends = ['Fold {}'.format(i + 1) for i in range(util.NUM_VAL_FOLDS)]
fold_idx = 0
for tr, te in folds:
# Load and setup model
model = neural.create_network(model_name, args,
(1, args.roi_size, args.roi_size))
model.network.summary()
model.name = model.name + '.fold-{}'.format(fold_idx + 1)
if args.load_model:
print "Loading model: data/{}".format(model.name)
model.load('data/' + model.name)
# Train/test model
froc = evaluate_model(model, blobs[tr], pred_blobs[tr], rois[tr],
blobs_val[te], pred_blobs[te], rois_val[te],
args.load_model)
frocs.append(froc)
# Record model results
current_frocs = [eval.average_froc([froc_i]) for froc_i in frocs]
util.save_froc(current_frocs,
'data/{}-{}-folds-froc'.format(model.name[:-7],
args.detector),
legends[:len(frocs)],
with_std=False)
model.save('data/' + model.name)
fold_idx += 1
legends = ['Val FROC (LIDC-IDRI)']
average_froc = eval.average_froc(frocs, np.linspace(0.0, 10.0, 101))
util.save_froc([average_froc],
'data/{}-{}-val-froc'.format(model.name[:-7],
args.detector),
legends,
with_std=True)
def exp_eval_ots_lidc_jsrt(args):
# load LIDC & JSRT-positives data
imgs_tr, blobs_tr = lidc.load()
pred_blobs_tr = detect.read_blobs('data/{}-lidc-pred-blobs.pkl'.format(
args.detector))
masks_tr = np.load('data/aam-lidc-pred-masks.npy')
imgs_te, blobs_te = jsrt.load(set_name='jsrt140p')
pred_blobs_te = detect.read_blobs('data/{}-jsrt140p-pred-blobs.pkl'.format(
args.detector))
masks_te = np.load('data/aam-jsrt140p-pred-masks.npy')
rois_tr = lnd.create_rois(imgs_tr, masks_tr, pred_blobs_tr, args)
rois_te = lnd.create_rois(imgs_te, masks_te, pred_blobs_te, args)
# Create rois dataset
rois_tr, Y_tr, _, _ = neural.create_train_test_sets(
blobs_tr, pred_blobs_tr, rois_tr, None, None, None)
generator = augment.get_default_generator((args.roi_size, args.roi_size))
rois_tr, Y_tr = augment.balance_and_perturb(rois_tr, Y_tr, generator)
range_tr = np.max(rois_tr), np.min(rois_tr)
print "range {}".format(range_tr)
# Extract features
network = VGG16(mode='ots-feat', pool_layer=args.pool_layer)
feats_tr = extract_convfeats(network, rois_tr, range_tr)
feats_te = extract_convfeats(network, rois_te, range_tr)
np.save('data/{}-lidc-feats.npy'.format(args.detector, fold_idx), feats_tr)
np.save('data/{}-jsrt140p-feats.npy'.format(args.detector, fold_idx),
feats_te)
# Eval classifier
clf = LinearSVC(C=args.svm_c)
froc = evaluate_classifier(clf, feats_tr, Y_tr, blobs_te, pred_blobs_te,
feats_te)
def model_selection_with_convfeats(args):
# Load img, blobs and masks
imgs, blobs, paths = lidc.load(pts=True, set_name=args.ds_tr)
pred_blobs = detect.read_blobs('data/{}-lidc-pred-blobs.pkl'.format(
args.detector))
masks = np.load('data/aam-lidc-pred-masks.npy')
assert len(imgs) == len(masks) and len(pred_blobs) == len(masks)
# Load folds
folds = util.model_selection_folds(imgs)
# Create rois
#rois = create_rois(imgs, masks, pred_blobs, args, real_blobs=blobs)
# Set up CV
frocs = []
legends = ['Fold {}'.format(i + 1) for i in range(util.NUM_VAL_FOLDS)]
fold_idx = 0
import time
from sklearn.neighbors import KNeighborsClassifier
for tr, te in folds:
print "Load features fold {}".format(fold_idx)
start = time.time()
feats_tr, Y_tr = load_features('data/{}-f{}-lidc-feats'.format(
args.detector, fold_idx))
print 'tr time {}'.format(time.time() - start)
start = time.time()
feats_te = np.load('data/{}-f{}-te-lidc-feats.npy'.format(
args.detector, fold_idx))
print 'te time {}'.format(time.time() - start)
print "-> tr {}, {}, te {}".format(feats_tr.shape, Y_tr.shape,
feats_te.shape)
# Train/test model
print "Evaluate clf"
#clf = KNeighborsClassifier(n_neighbors=3)
clf = LinearSVC(C=args.svm_C)
froc = evaluate_classifier(clf, feats_tr, Y_tr, blobs[te],
pred_blobs[te], feats_te)
frocs.append(froc)
# Record model results
current_frocs = [eval.average_froc([froc_i]) for froc_i in frocs]
util.save_froc(current_frocs,
'data/lsvm-z-C{}-{}-folds-froc'.format(
args.svm_C, args.detector),
legends[:len(frocs)],
with_std=False)
fold_idx += 1
legends = ['Val FROC (LIDC-IDRI)']
average_froc = eval.average_froc(frocs, np.linspace(0.0, 10.0, 101))
util.save_froc([average_froc],
'data/lsvm-z-C{}-{}-val-froc'.format(
args.svm_C, args.detector),
legends,
with_std=True)
def model_evaluation_tr_lidc_te_jsrt(model_name, args):
imgs_tr, blobs_tr = lidc.load()
pred_blobs_tr = detect.read_blobs('data/{}-lidc-pred-blobs.pkl'.format(
args.detector))
masks_tr = np.load('data/aam-lidc-pred-masks.npy')
imgs_te, blobs_te = jsrt.load(set_name='jsrt140p')
pred_blobs_te = detect.read_blobs('data/{}-jsrt140p-pred-blobs.pkl'.format(
args.detector))
masks_te = np.load('data/aam-jsrt140p-pred-masks.npy')
rois_tr = create_rois(imgs_tr, masks_tr, pred_blobs_tr, args)
rois_te = create_rois(imgs_te, masks_te, pred_blobs_te, args)
model = neural.create_network(model_name, args,
(1, args.roi_size, args.roi_size))
model.name += '-{}-lidc'.format(args.detector)
froc = evaluate_model(model, blobs_tr, pred_blobs_tr, rois_tr, blobs_te,
pred_blobs_te, rois_te)
froc = eval.average_froc([froc])
legends = ['Test FROC (JSRT positives)']
util.save_froc([froc],
'data/{}-{}-lidc-jsrt-froc'.format(model.name,
args.detector),
legends,
with_std=False)
def eval_trained_model(model_name, args):
imgs, blobs = lidc.load()
pred_blobs = detect.read_blobs('data/{}-lidc-pred-blobs.pkl'.format(
args.detector))
masks = np.load('data/aam-lidc-pred-masks.npy')
folds = util.model_selection_folds(imgs)
rois = create_rois(imgs, masks, pred_blobs, args)
save_performance_history(model_name, args, rois, blobs, pred_blobs, folds)
def eval_by_missed_nodules():
images, blobs = lidc.load()
masks = np.load('data/aam-lidc-pred-masks.npy')
p = 0
tp = 0
for i in range(len(images)):
assert masks[i].shape[:2] == images[i][0].shape[:2]
p += len(blobs[i])
for j in range(len(blobs[i])):
if masks[i][blobs[i][j][0], blobs[i][j][1]] > 0:
tp += 1
print 'Total nodules {}, missed nodules {}'.format(p, p - tp)
def segment_datasets(model_name):
model = pickle.load(open('data/{}-{}-model.pkl'.format(model_name, 'jsrt140n'), 'rb'))
print('Segment lidc')
lidc_images, _ = lidc.load()
pred_masks = model.transform(lidc_images)
np.save('data/{}-{}-pred-masks'.format(model_name, 'lidc'), np.array(pred_masks))
print('Segment jsrt positives')
jsrt_images, _ = jsrt.load(set_name='jsrt140p')
pred_masks = model.transform(jsrt_images)
np.save('data/{}-{}-pred-masks'.format(model_name, 'jsrt140p'), np.array(pred_masks))
'''
def detection_vs_distance(method, args):
images, blobs = lidc.load()
masks = np.load('data/aam-lidc-pred-masks.npy')
pred_blobs, probs = detect_blobs(images,
masks,
args.threshold,
real_blobs=blobs,
method=method)
write_blobs(
pred_blobs,
'data/{}-{}-aam-lidc-pred-blobs.pkl'.format(method, args.threshold))
froc = eval.froc(blobs, pred_blobs, probs, distance='rad')
froc = eval.average_froc([froc], DETECTOR_FPPI_RANGE)
def save_blobs(method, args):
images, blobs = lidc.load()
masks = np.load('data/aam-lidc-pred-masks.npy')
print 'masks shape {}'.format(masks.shape)
pred_blobs, probs = detect_blobs(images,
masks,
args.threshold,
real_blobs=blobs,
method=method)
write_blobs(
pred_blobs,
'data/{}-{}-aam-lidc-pred-blobs.pkl'.format(method, args.threshold))
froc = eval.froc(blobs, pred_blobs, probs, distance='rad')
froc = eval.average_froc([froc], DETECTOR_FPPI_RANGE)
abpi = average_bppi(pred_blobs)
util.save_froc([froc],
'data/{}-{}-lidc-blobs-froc'.format(method, args.threshold),
['{} FROC on LIDC dataset'.format(method)],
fppi_max=100)
np.savetxt('data/{}-{}-lidc-blobs-abpi.txt'.format(method, args.threshold),
np.array([abpi]))
print('Average blobs per image LIDC {}'.format(abpi))
images, blobs = jsrt.load(set_name='jsrt140p')
masks = np.load('data/aam-jsrt140p-pred-masks.npy')
pred_blobs, probs = detect_blobs(images,
masks,
args.threshold,
real_blobs=blobs,
method=method)
write_blobs(
pred_blobs, 'data/{}-{}-aam-jsrt140p-pred-blobs.pkl'.format(
method, args.threshold))
froc = eval.froc(blobs, pred_blobs, probs, distance='rad')
froc = eval.average_froc([froc], DETECTOR_FPPI_RANGE)
abpi = average_bppi(pred_blobs)
util.save_froc([froc],
'data/{}-{}-jsrt140p-blobs-froc'.format(
method, args.threshold),
['{} FROC on JSRT140 positives dataset'.format(method)],
fppi_max=100)
np.savetxt(
'data/{}-{}-jsrt140p-blobs-abpi.txt'.format(method, args.threshold),
np.array([abpi]))
print('Average blobs per image JSRT positives {}'.format(abpi))
def save_rois(args):
imgs_tr, blobs_tr = lidc.load(pts=False)
pred_blobs_tr = detect.read_blobs('data/sbf-aam-lidc-pred-blobs.pkl')
masks_tr = np.load('data/aam-lidc-pred-masks.npy')
imgs_te, blobs_te = jsrt.load(set_name='jsrt140p')
pred_blobs_te = detect.read_blobs('data/sbf-aam-jsrt140p-pred-blobs.pkl')
masks_te = np.load('data/aam-jsrt140p-pred-masks.npy')
rois_tr = create_rois(imgs_tr,
masks_tr,
pred_blobs_tr,
args,
real_blobs=blobs_tr)
rois_te = create_rois(imgs_te,
masks_te,
pred_blobs_te,
args,
real_blobs=blobs_te)
X_tr, Y_tr, X_te, Y_te = neural.create_train_test_sets(
blobs_tr, pred_blobs_tr, rois_tr, blobs_te, pred_blobs_te, rois_te)
X_tr, Y_tr = util.split_data_pos_neg(X_tr, Y_tr)
X_te, Y_te = util.split_data_pos_neg(X_te, Y_te)
X_pos = X_tr[0]
idx = np.random.randint(0, len(X_tr[1]), len(X_pos))
X_neg = X_tr[1][idx]
print len(X_pos), len(X_neg)
for i in range(len(X_pos)):
util.imwrite('data/lidc/roi{}p.jpg'.format(i), X_pos[i][0])
np.save('data/lidc/roi{}p.npy'.format(i), X_pos[i])
util.imwrite('data/lidc/roi{}n.jpg'.format(i), X_neg[i][0])
np.save('data/lidc/roi{}n.npy'.format(i), X_neg[i])
X_pos = X_te[0]
idx = np.random.randint(0, len(X_te[1]), len(X_pos))
X_neg = X_te[1][idx]
print len(X_pos), len(X_neg)
for i in range(len(X_pos)):
util.imwrite('data/jsrt140/roi{}p.jpg'.format(i), X_pos[i][0])
np.save('data/jsrt140/roi{}p.npy'.format(i), X_pos[i])
util.imwrite('data/jsrt140/roi{}n.jpg'.format(i), X_neg[i][0])
np.save('data/jsrt140/roi{}n.npy'.format(i), X_neg[i])
def model_selection_unsup(model_name, args):
imgs, blobs, paths = lidc.load(pts=True)
pred_blobs = detect.read_blobs('data/{}-lidc-pred-blobs.pkl'.format(
args.detector))
masks = np.load('data/aam-lidc-pred-masks.npy')
assert len(imgs) == len(masks) and len(pred_blobs) == len(masks)
folds = util.model_selection_folds(imgs)
rois = create_rois(imgs, masks, pred_blobs, args, real_blobs=blobs)
frocs = []
legends = ['Fold {}'.format(i + 1) for i in range(util.NUM_VAL_FOLDS)]
fold_idx = 0
for tr, te in folds:
model = neural.create_network(model_name, args,
(1, args.roi_size, args.roi_size))
model.name = model.name + '.fold-{}'.format(fold_idx + 1)
froc = evaluate_model(model, blobs[tr], pred_blobs[tr], rois[tr],
blobs[te], pred_blobs[te], rois[te])
frocs.append(froc)
current_frocs = [eval.average_froc([froc_i]) for froc_i in frocs]
util.save_froc(current_frocs,
'data/{}-{}-folds-froc'.format(model_name,
args.detector),
legends[:len(frocs)],
with_std=False)
model.save('data/' + model.name)
fold_idx += 1
legends = ['Val FROC (LIDC-IDRI)']
average_froc = eval.average_froc(frocs, np.linspace(0.0, 10.0, 101))
util.save_froc([average_froc],
'data/{}-{}-val-froc'.format(model_name, args.detector),
legends,
with_std=True)
def extract_features_from_convnet(args): # Load img, blobs and masks
imgs, blobs, paths = lidc.load(pts=True, set_name=args.ds_tr)
pred_blobs = detect.read_blobs('data/{}-lidc-pred-blobs.pkl'.format(
args.detector))
masks = np.load('data/aam-lidc-pred-masks.npy')
assert len(imgs) == len(masks) and len(pred_blobs) == len(masks)
# Load folds
folds = util.model_selection_folds(imgs)
# Create rois
rois = lnd.create_rois(imgs, masks, pred_blobs, args, real_blobs=blobs)
# Load model
network = VGG16(mode='ots-feat', pool_layer=args.pool_layer)
network.summary()
# Set up CV
frocs = []
legends = ['Fold {}'.format(i + 1) for i in range(util.NUM_VAL_FOLDS)]
fold_idx = 0
for tr, te in folds:
# TODO: apply extract convfeats funcs for tr and te sets
print "Fold {}".format(fold_idx + 1)
X_tr, Y_tr, _, _ = neural.create_train_test_sets(
blobs[tr], pred_blobs[tr], rois[tr], blobs[te], pred_blobs[te],
rois[te])
gc.collect()
generator = augment.get_default_generator(
(args.roi_size, args.roi_size))
X_tr, Y_tr = augment.balance_and_perturb(X_tr, Y_tr, generator)
gc.collect()
''''
counta = 0
countb = 0
count = 0
while counta < 10 and countb < 10:
if Y_tr[count][1] > 0 and counta < 10:
util.imshow("positives", X_tr[count][0], display_shape=(256, 256))
counta += 1
elif Y_tr[count][1] == 0.0 and countb < 10:
util.imshow("negatives", X_tr[count][0], display_shape=(256, 256))
countb += 1
count += 1
'''
range_tr = (X_tr.min(), X_tr.max())
print "Range {}".format(range_tr)
print "Extract feats on balanced tr set"
feats_tr = extract_convfeats(network, X_tr, range_tr)
save_features(
"data/vgg16-{}-{}-f{}-lidc-feats".format(args.pool_layer,
args.detector, fold_idx),
feats_tr, Y_tr)
gc.collect()
print "Extract feats on te set"
feats_te = extract_convfeats_from_rois(network, rois[te], range_tr)
print "Test feats to save shape {}".format(feats_te.shape)
np.save(
"data/vgg16-{}-{}-f{}-te-lidc-feats.npy".format(
args.pool_layer, args.detector, fold_idx), feats_te)
gc.collect()
fold_idx += 1