Sheng Liu*, Kangning Liu*, Weicheng Zhu, Yiqiu Shen, Carlos Fernandez-Granda
(* The first two authors contribute equally, order decided by coin flipping.)
Official Implementation of Adaptive Early-Learning Correction for Segmentation from Noisy Annotations (CVPR 2022 Oral)
- Install python dependencies.
pip install -r requirements.txt
- Downlaod the SegTHOR dataset and conduct data preprocessing, resize all the image to the size of 256*256 using linear interpolation of opencv_python (INTER_LINEAR).
The details of public SegTHOR dataset can be found in this link.
In this study, we randomly assign patients in the original training set into training, validation, and test set using following scheme:
- training set: ['Patient_01', 'Patient_02', 'Patient_03', 'Patient_04', 'Patient_05', 'Patient_06', 'Patient_07', 'Patient_09', 'Patient_10', 'Patient_11', 'Patient_12', 'Patient_13', 'Patient_14', 'Patient_15', 'Patient_16', 'Patient_17', 'Patient_18', 'Patient_19', 'Patient_20', 'Patient_22', 'Patient_24', 'Patient_25', 'Patient_26', 'Patient_28', 'Patient_30', 'Patient_31', 'Patient_33', 'Patient_36', 'Patient_38', 'Patient_39', 'Patient_40']
- validation set: ['Patient_21', 'Patient_23', 'Patient_27', 'Patient_29', 'Patient_37']
- test set: ['Patient_08', 'Patient_27', 'Patient_32', 'Patient_34', 'Patient_35']
We used only slices that contain foreground class and downsampled all slices into 256 * 256 pixels using linear interpolation.
Here is the example script of ADELE:
python3 brat/train_segthor.py \
--cache-dir DIR_OF_THE_DATA \
--data-list DIR_OF_THE_DATALIST \
--save-dir MODEL_SAVE_DIR \
--model-name MODEL_NAME \
--seed 0 \
--jsd-lambda 1 \
--rho 0.8 \
--label-correction \
--tau_fg 0.7 \
--tau_bg 0.7 \
--r 0.9
where the arguments represent:
cache-dir- Parent dir of the datalist, tr.pkl, val.pkl, ts.pkl, which are the input data for training, validation and testing set.data-list- Parent dir of the data_list.pkl file, which is the list of names for the input data.save-dir- Folder, where models and results will be saved.model-name- Name of the model.seed- the random seed of the noise realization, default 0.jsd-lambda- the consistency strength, if set to 0, no consistency regularization will be applied, default 1.rho- consistency confidence threshold, this is the threshold on the confidence of model's prediction to decide which examples are applied with consistency regularizationlabel-correction- whether to conduct label correction, if set this arguments, the model will do label correction, default False.tau_fg, tau_bg- label correction confidence threshold for foreground and background, in the main paper and all the experiment, we set these two values to be the same for simplicity, default 0.7.r- curve fitting threshold to control when a specific semantic category will be corrected, default 0.9.
Here is the example script of baseline:
python3 brat/train_segthor.py \
--cache-dir DIR_OF_THE_DATA \
--data-list DIR_OF_THE_DATALIST \
--save-dir MODEL_SAVE_DIR \
--model-name MODEL_NAME \
--seed 0 \
--jsd-lambda 0
we provide the trained model in the following link
https://drive.google.com/file/d/10cTOraETOmb2jOCJ4E0m_y9lrjrA3g2u/view?usp=sharing
The training code will be released soon, inference code is the same as the official code for SEAM. Attach the Code link provide by the SEAM author: https://github.com/YudeWang/semantic-segmentation-codebase/tree/main/experiment/seamv1-pseudovoc