lukaszbinden / pediatric-bone-age-prediction

Based on kaggle RSNA Pediatric Bone Age Prediction from X-Rays

Pediatric Bone Age Prediction

Based on kaggle RSNA Bone Age Prediction from X-Rays

• http://rsnachallenges.cloudapp.net/competitions/4
• https://www.kaggle.com/kmader/rsna-bone-age

Results of transfer learning experiments

Pretrain the model on the much larger chest NIH dataset (112k images) and finetune on the small bone age dataset (14k). The model is based on the RSNA challenge winner model by 16Bit. See folder 'experiments' for source code, e.g. experiment_age_range.py.

Experiment	Chest training size	Epochs	MAE	Date
16Bit challenge winner	n/a	500	4.265	2017
Radiologist performance	n/a	n/a	7.32	2017
Imagenet	n/a	50	76.8	181020
Imagenet	n/a	250	8.8	181029
No TL, random init.	n/a	250	10.8	181030
Chest 0-20yrs., 30 layers finetuning	1560	50	33.9	181022
Chest 0-20yrs., 100 layers finetuning	1560	50	37	181022
Chest 0-20yrs., 100 layers finetuning	1560	250	41.8	181024
Chest 0-20yrs., 50 layers finetuning	1560	250	34.5	181025
Chest 0-20yrs., 20 layers finetuning	1560	250	36.7	181026
Chest 0-20yrs., 30 layers finetuning	1560	250	35.8	181027
Chest 0-100yrs, 3 layers finetuning	89696	250	34.8	181103
Chest 0-100yrs, all layers finetuning	89696	250	pending*	pending*

The training/validation split is 80/20. The idea to restrict the chest X-rays patient's age to the same range as the hand X-rays patients turned out unsuccessful as the dataset size decreased significantly to 1560 images and transfer learning results were disappointing.
* After 14 days of training, this experiment crashed on 12/11/18 due to a "bus error" on the cluster. Due to lack of checkpoints, need to restart it from scratch.

Experiments

• Try transfer learning with other medical datasets (MURA stanford, etc. )
• Try different architectures of the used net, e.g. use DenseNet instead of InceptionV3
• Build model on top of model to predict accuracy of age model
• Try combining different architectures/approaches with meta learning (e.g. voting)
• Chest XRays validate against disease and patient age respectively
• Experiment with different number of freezed layers
• Do more extensive hyperparameter tuning
• Regression vs. Classification on months range between 0 and 12 * 100
• Pretrain Kevin's baseline with NIH chest dataset (transfer learning)

Links

• MURA dataset: https://stanfordmlgroup.github.io/projects/mura/
• Chest Xrays dataset on box (full set): https://nihcc.app.box.com/v/ChestXray-NIHCC
• Chest Xrays dataset on kaggle (5% sample): https://www.kaggle.com/nih-chest-xrays/data
• Bone Age dataset: https://stanfordmedicine.app.box.com/s/4r1zwio6z6lrzk7zw3fro7ql5mnoupcv/folder/42459416739
• RSNA Bone Age on kaggle: https://www.kaggle.com/kmader/rsna-bone-age
• Different architectures to try out: https://keras.io/applications/
• 2017 RSNA pediatric bone age challenge: http://rsnachallenges.cloudapp.net/competitions/4
• 16Bit challenge winner: https://www.16bit.ai/blog/ml-and-future-of-radiology

pediatric-bone-age-prediction's Issues

update readme

which file is which experiment on the table etc.? when I looking that it's hard to me. please update readme. thanks :)

Question

please,, help me, how could I combine 3 numerical vectors with image features vectors using a generator, what is wrong in this code

def combined_generators(image_generator, x_data, y_data, z_data, batch_size):
x_gen = cycle(batch(x_data, batch_size))
y_gen = cycle(batch(y_data, batch_size))
z_gen = cycle(batch(z_data, batch_size))

while True:
    nextImage = next(image_generator)
    
    nextx = next(x_gen)
    assert len(nextImage[0]) == len(nextx)

    nexty = next(y_gen)
    assert len(nextImage[0]) == len(nexty)

    nextz = next(z_gen)
    assert len(nextImage[0]) == len(nextz)
    
    yield [nextImage[0], nextx, nexty, nextz], nextImage[1]

def batch(iterable, n=1):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)]

train_gen_wrapper = combined_generators(train_batches, df['x'], df['y'], df['z'], 16)
val_gen_wrapper = combined_generators(valid_batches, dft['x'], dft['y'], dft['z'], 16)