Weakly-Supervised-3D-Classification-of-Chest-CT-using-Aggregated-Multi-Resolution-Deep-Segmentation-Features
This Repo contains the updated implementation of our paper “Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features”, Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020).
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Version-1: Implemented Segmentation Module and Classification Seperately and was in Tensorflow 1.x
Can be seen here: https://github.com/anindox8/Deep-Segmentation-Features-for-Weakly-Supervised-3D-Disease-Classification-in-Chest-CT
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Version-2: Updated the Implementation , For reducing computation expenses the Segmenation Module and Classifiction Module is combined,updated implementation is in Tensorflow 2.0. This implemnetation is 2 times faster than the Version-1 in terms of training. Also Project has been moved from multi-class to multi-label classification setup (Follow the SPIE presentation for clear idea).
If our work help in your task or project please site the work at (https://doi.org/10.1117/12.2550857). This work is been presented at SPIE Medical Imaging, 2020, Houston, Texas, United States. Presentation can be seen here : https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11314/2550857/Weakly-supervised-3D-classification-of-chest-CT-using-aggregated-multi/10.1117/12.2550857.full?SSO=1
Citation
Anindo Saha*, Fakrul I. Tushar*, Khrystyna Faryna, Vincent M. D'Anniballe, Rui Hou,
Maciej A. Mazurowski, Geoffrey D. Rubin M.D., and Joseph Y. Lo
"Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features",
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis,1131408 (16 March 2020);
https://doi.org/10.1117/12.2550857
(*Authors with equal contribution to this work.)
Directories and Files
- i) DyFA -|–> Dynamic Feature aggragation Model and training script.
a) config.py |-- Configuration file to train the DyFA model
b) DenseVnet3D.py |-- 3D implementation of the DenseVnet (Segmentation Module)
c) DyFA_Model.py |-- DyFA model (Segmentation+Classification Module)
d) loss_funnction_And_matrics |-- Loss Function.
e) resume_training_using_check_point |-- Training Script
f) tfrecords_utilities |-- Tfrecords decoding function
- ii) SyFa -|–> Static Feature aggragation Model and training script.
a) config.py |-- Configuration file to train the DyFA model
b) DenseVnet3D.py |-- 3D implementation of the DenseVnet (Segmentation Module)
c) StFA_Model.py |-- StFA model (Segmentation+Classification Module)
d) loss_funnction_And_matrics |-- Loss Function.
e) resume_training_using_check_point |-- Training Script
f) tfrecords_utilities |-- Tfrecords decoding function
- iii) Figure -|–> Figure used in this Repo
- iv) SPIE_presentation -|–> SPIE presentation
How to run
To run the model all is to need to configure the config.py based on your requiremnet. and use the command
import tensorflow as tf
from loss_funnction_And_matrics import*
import math
###---Number-of-GPU
NUM_OF_GPU=2
#["gpu:1","gpu:2","gpu:3"]
DISTRIIBUTED_STRATEGY_GPUS=["gpu:0","gpu:1"]
###-----SEGMENATTION----###
SEGMENTATION_MODEL_PATH='/Path/of/the/Segmentation Module/weight/Model.h5'.h5'
SEGMENTATION_NUM_OF_CLASSES=31
#####-----Configure DenseVnet3D---##########
SEG_NUMBER_OF_CLASSES=31
SEG_INPUT_PATCH_SIZE=(128,160,160, 1)
NUM_DENSEBLOCK_EACH_RESOLUTION=(4, 8, 16)
NUM_OF_FILTER_EACH_RESOLUTION=(12,24,24)
DILATION_RATE=(5, 10, 10)
DROPOUT_RATE=0.25
###----Resume-Training
'''
if want to resume training from the weights Set
RESUME_TRAINING=1
'''
RESUME_TRAINING=0
RESUME_TRAIING_MODEL='/Path/of/the/model/weight/Model.h5'
TRAINING_INITIAL_EPOCH=0
##Network Configuration
NUMBER_OF_CLASSES=5
INPUT_PATCH_SIZE=(128,160,160, 1)
TRAIN_NUM_RES_UNIT=3
TRAIN_NUM_FILTERS=(16, 32, 64, 128)
TRAIN_STRIDES=((1, 1, 1), (2, 2, 2), (2, 2, 2), (2, 2, 2))
TRAIN_CLASSIFY_ACTICATION=tf.nn.relu6
TRAIN_KERNAL_INITIALIZER=tf.keras.initializers.VarianceScaling(distribution='uniform')
##Training Hyper-Parameter
##Training Hyper-Parameter
TRAIN_CLASSIFY_LEARNING_RATE =1e-4
TRAIN_CLASSIFY_LOSS=Weighted_BCTL
OPTIMIZER=tf.keras.optimizers.Adam(lr=TRAIN_CLASSIFY_LEARNING_RATE,epsilon=1e-5)
TRAIN_CLASSIFY_METRICS=tf.keras.metrics.AUC()
BATCH_SIZE=12
TRAINING_STEP_PER_EPOCH=math.ceil((3514)/BATCH_SIZE)
VALIDATION_STEP=math.ceil((759)/BATCH_SIZE)
TRAING_EPOCH=300
NUMBER_OF_PARALLEL_CALL=6
PARSHING=3*BATCH_SIZE
#--Callbacks-----
ModelCheckpoint_MOTITOR='val_loss'
TRAINING_SAVE_MODEL_PATH='/Path/to/save/model/weight/Model.h5'
TRAINING_CSV='DyFA_61FC1X1_April17_2020.csv'
LOG_NAME="Log_DyFA_61FC1X1_April17_2020"
MODEL_SAVING_NAME="DyFAModel61FC1X1_{val_loss:.2f}_{epoch}.h5"
####
TRAINING_TF_RECORDS='/Training/tfrecords/path/'
VALIDATION_TF_RECORDS='/Val/tfrecords/path/'
Multi-label Data Statistics
Results
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