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update examples for ssl
add AtriaSeg
1 parent 8be1f14 commit 55bfa53

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‎README.md‎

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The latest released version of PyMIC can be installed by:
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```bash
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pip install PYMIC
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pip install PYMIC==0.3.1.1
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```
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To use the latest development version, you can download the source code [here][PyMIC_link], and install it by:
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|Fully supervised segmentation|[JSRT2][JSRT2_link]|Using a customized network and loss function for the JSRT dataset|
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|Fully supervised segmentation|[Fetal_HC][fetal_hc_link]|Using a 2D UNet for fetal head segmentation from 2D ultrasound images|
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|Fully supervised segmentation|[Prostate][prostate_link]|Using a 3D UNet for prostate segmentation from 3D MRI|
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|Semi-supervised segmentation|[seg_ssl/ACDC][ssl_acdc_link]|Comparing different semi-supervised methods for heart structure segmentation|
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|Semi-supervised segmentation|[seg_ssl/ACDC][ssl_acdc_link]|Semi-supervised methods for heart structure segmentation using 2D CNNs|
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|Semi-supervised segmentation|[seg_ssl/AtriaSeg][ssl_atrial_link]|Semi-supervised methods for left atrial segmentation using 3D CNNs|
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|Weakly-supervised segmentation|[seg_wsl/ACDC][wsl_acdc_link]|Segmentation of heart structure with scrible annotations|
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|Noisy label learning|[seg_nll/JSRT][nll_jsrt_link]|Comparing different NLL methods for learning from noisy labels|
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[fetal_hc_link]:segmentation/fetal_hc
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[prostate_link]:segmentation/prostate
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[ssl_acdc_link]:seg_ssl/ACDC
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[ssl_atrial_link]:seg_ssl/AtriaSeg/
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[wsl_acdc_link]:seg_wsl/ACDC
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[nll_jsrt_link]:seg_nll/JSRT
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‎seg_nll/JSRT/clslsr_get_confidence.py‎

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# -*- coding: utf-8 -*-
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from __future__ import print_function, division
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import sys
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from pymic.net_run_nll.nll_clslsr import get_confidence_map
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if __name__ == "__main__":
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"""
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The main function to get the confidence map during inference.
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"""
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if(len(sys.argv) < 2):
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print('Number of arguments should be 2. e.g.')
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print(' python nll_clslsr.py config.cfg')
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exit()
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cfg_file = str(sys.argv[1])
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get_confidence_map(cfg_file)

‎seg_ssl/AtriaSeg/config/data/image_test.csv‎

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image,label
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‎seg_ssl/AtriaSeg/config/data/image_test_gt_seg.csv‎

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ground truth,segmentation
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‎seg_ssl/AtriaSeg/config/data/image_train.csv‎

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‎seg_ssl/AtriaSeg/config/data/image_train_r10_lab.csv‎

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‎seg_ssl/AtriaSeg/config/data/image_train_r10_unlab.csv‎

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‎seg_ssl/AtriaSeg/config/data/image_valid.csv‎

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image,label
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‎seg_ssl/AtriaSeg/config/evaluation.cfg‎

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[evaluation]
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metric = dice
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label_list = [1]
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organ_name = atrial
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ground_truth_folder_root = ../../PyMIC_data/AtriaSeg/TrainingSet_crop/
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segmentation_folder_root = [result/unet3d_r10_baseline]
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evaluation_image_pair = config/data/image_test_gt_seg.csv
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‎seg_ssl/AtriaSeg/config/unet3d_r10_baseline.cfg‎

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[dataset]
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# tensor type (float or double)
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tensor_type = float
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task_type = seg
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root_dir = ../../PyMIC_data/AtriaSeg/TrainingSet_crop/
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train_csv = config/data/image_train_r10_lab.csv
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valid_csv = config/data/image_valid.csv
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test_csv = config/data/image_test.csv
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train_batch_size = 2
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# data transforms
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train_transform = [RandomCrop, RandomFlip, NormalizeWithMeanStd, GammaCorrection, GaussianNoise, LabelToProbability]
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valid_transform = [NormalizeWithMeanStd, LabelToProbability]
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test_transform = [NormalizeWithMeanStd]
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RandomCrop_output_size = [72, 96, 112]
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RandomCrop_foreground_focus = False
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RandomCrop_foreground_ratio = None
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Randomcrop_mask_label = None
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RandomFlip_flip_depth = True
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RandomFlip_flip_height = True
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RandomFlip_flip_width = True
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NormalizeWithMeanStd_channels = [0]
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GammaCorrection_channels = [0]
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GammaCorrection_gamma_min = 0.7
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GammaCorrection_gamma_max = 1.5
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GaussianNoise_channels = [0]
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GaussianNoise_mean = 0
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GaussianNoise_std = 0.05
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GaussianNoise_probability = 0.5
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[network]
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# this section gives parameters for network
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# the keys may be different for different networks
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# type of network
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net_type = UNet3D
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# number of class, required for segmentation task
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class_num = 2
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in_chns = 1
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feature_chns = [32, 64, 128, 256]
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dropout = [0.0, 0.0, 0.5, 0.5]
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trilinear = True
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multiscale_pred = False
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[training]
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# list of gpus
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gpus = [1]
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loss_type = [DiceLoss, CrossEntropyLoss]
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loss_weight = [0.5, 0.5]
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# for optimizers
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optimizer = Adam
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learning_rate = 1e-3
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momentum = 0.9
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weight_decay = 1e-5
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# for lr schedular
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lr_scheduler = ReduceLROnPlateau
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lr_gamma = 0.5
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ReduceLROnPlateau_patience = 3000
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early_stop_patience = 5000
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ckpt_save_dir = model/unet3d_baseline
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iter_max = 20000
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iter_valid = 100
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iter_save = 20000
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[testing]
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# list of gpus
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gpus = [0]
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# checkpoint mode can be [0-latest, 1-best, 2-specified]
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ckpt_mode = 1
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output_dir = result/unet3d_baseline
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post_process = None
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sliding_window_enable = False

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