This repository contains the implementation for the paper: Kadir M.A, Alam H. T, Sonntag D., "EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation" [MICAAI2023]
train_active.py [-h] [--model {deeplab, unet, y_net_gen, y_net_gen_ffc}]
[--backbone {resnet, xception, drn, mobilenet}]
[--out-stride OUT_STRIDE]
[--dataset {duke, UMN, AROI}]
[--workers N] [--base-size BASE_SIZE]
[--sync-bn SYNC_BN] [--loss-type {'ce', 'focal', 'ce+dice'}]
[--epochs N] [--batch-size N] [--use-balanced-weights]
[--lr LR] [--lr-scheduler {step}]
[--optimizer {SGD,Adam}] [--step-size STEP_SIZE]
[--use-lr-scheduler] [--momentum M] [--weight-decay M]
[--nesterov] [--gpu-ids GPU_IDS] [--seed S]
[--checkname CHECKNAME] [--eval-interval EVAL_INTERVAL]
[--memory-hog]
[--max-iterations MAX_ITERATIONS]
[--active-selection-size ACTIVE_SELECTION_SIZE]
[--region-size REGION_SIZE]
[--region-selection-mode REGION_SELECTION_MODE]
[--view-entropy-mode {soft,vote,mc_dropout}]
[--active-selection-mode {random, 'edgeal_region'}]
[--superpixel-dir SUPERPIXEL_DIR]
[--superpixel-coverage-dir SUPERPIXEL_COVERAGE_DIR]
[--superpixel-overlap SUPERPIXEL_OVERLAP]
[--start-entropy-threshold START_ENTROPY_THRESHOLD]
[--entropy-change-per-selection ENTROPY_CHANGE_PER_SELECTION]
Run --help for more details.
# sample dataset
python train_active.py --model y_net_gen_ffc --g-ratio 0.5 --dataset duke --workers 2 --epochs 5 --eval-interval 2 --batch-size=10 --lr 5e-4 --weight-decay 1e-4 --optimizer Adam --use-lr-scheduler --lr-scheduler step --step-size 100 --checkname view_entropy_ynet+fcc_0_lr-0.0004_bs-6_ep-60_wb-0_lrs-0_240x240ce+dice --base-size 224,224 --loss-type ce+dice --max-iterations 10 --active-selection-size 7 --active-selection-mode edgeal_region --region-selection-mode superpixel
##### Random
python train_active.py --model y_net_gen_ffc --g-ratio 0.5 --dataset duke --workers 2 --epochs 100 --eval-interval 1 --batch-size=10 --lr 5e-4 --weight-decay 1e-4 --optimizer Adam --use-lr-scheduler --lr-scheduler step --step-size 100 --checkname random_ynet+fcc_0_lr-0.0004_bs-6_ep-60_wb-0_lrs-0_240x240focalsoft --base-size 224,224 --loss-type ce+dice --max-iterations 10 --active-selection-size 7 --active-selection-mode random
Overall code structure is as follows:
| File / Folder | Description |
|---|---|
| train_active.py | Training script for active learning methods |
| train.py | Training script for full dataset training |
| constants.py | Constants used across the code |
| argument_parser.py | Arguments parsing code |
| active_selection | Implementation of our method and other active learning methods for semantic segmentation |
| dataloader | Dataset classes |
| dataset | Train/test splits and raw files of datasets |
| model | DeeplabV3+ implementation (inspired from here) |
| utils | Misc utils |
The datasets must follow the following structure
dataset # root dataset directory
├── dataset-name
├── raw
├── selections
├── color # rgb frames
├── label # ground truth maps
├── edge # use any edge detection method / sovel operator
├── dct # cosign transform of image
├── info # camera intrinsics
├── superpixel # superpixel maps
├── coverage_superpixel # coverage maps
├── selections
├── seedset_0_frames.txt # seed set
├── train_frames.txt
├── val_frames.txt
├── test_frames.txt
├── dataset.lmdb # rgb frames + labels in lmdb format
To use this repository datasets must be in the structure described in last section. For creating the lmdb database, seed set, train / test splits and superpixel maps check helper scripts in dataset/preprocessing-scripts. We use this SEEDS implementation for generating superpixels (check this issue for troubleshooting). Further, to generate superpixel coverage maps (coverage_superpixel) check utils/superpixel_projections.py.
Cost-Effective Active Learning for Melanoma Segmentation
https://github.com/imatge-upc/medical-2017-nipsw
https://github.com/marc-gorriz/CEAL-Medical-Image-Segmentation