ILAWSIA

This is the code for the paper "Interactive Learning for Assisting Whole Slide Image Annotation" published at ACPR 2020.

http://cvit.iiit.ac.in/images/ConferencePapers/2021/Interactive_Learning.pdf

The paper has results from 2 datasets:

• NCT-CRC
• ICIAR

Get data

cd data

wget https://zenodo.org/record/1214456/files/NCT-CRC-HE-100K.zip
wget https://zenodo.org/record/1214456/files/CRC-VAL-HE-7K.zip

unzip NCT-CRC-HE-100K.zip
unzip CRC-VAL-HE-7K.zip

mv  CRC-VAL-HE-7K TestSet

python divide.py --root_dir CRC-HE-7K --query_dir QuerySet --search_dir SearchSet

Create required directories

We need to create result directory, checkpoint directory, log directory and a directory for storing embeddings generated at each iteration.

timestamp=`date +%s`
result_dir=data/ILAWSIA_results_$timestamp
ckpt_dir=data/ILAWSIA_ckpt_$timestamp
log_dir=data/ILAWSIA_logs_$timestamp
temp_dbdir=data/ILAWSIA_EmbDB_$timestamp

mkdir -p $result_dir $ckpt_dir $log_dir $temp_dbdir

Choose sampler

Now we need to select the sampler we want to use for the interactive learning

sampler_choice=cnfp

The choices for sampler and their briefs:

• entropy : Sample the images with highest entropy (the images model is most uncertain about)
• random : Random sampling
• front_mid_end : Sample from the front, mid and end of the ranked nearest neighbor list.
• cnfp: Acronym for "Closest Negative Farthest Positive". Sample from the closest negative and the farthest positive samples from the ranked nearest neighbor list. The negative and positive are the predictions as per classification model trained with already labelled images.
• hybrid: A hybrid of all the above sampling techniques.

Create the Frozen Query, Search and Test DB

  python resnet_featurizer.py \
			--root_dir data/QuerySet \
			--dest_dir data/QueryDBFrozen

  python resnet_featurizer.py \
			--root_dir data/SearchSet \
			--dest_dir data/SearchDBFrozen

  python resnet_featurizer.py \
			--root_dir data/TestSet \
			--dest_dir data/TestDBFrozen

Run interactive learning

  python interactive_learning.py \
			--query_dir data/QueryDBFrozen \
			--search_dir data/SearchDBFrozen \
			--test_dir data/TestDBFrozen \
			--temp_dbdir $temp_dbdir/EmbDB_$sampler_choice \
			--num_sessions 1000 \
			--rounds_per_session 5 \
			--expert_labels_per_round 10 \
			--sampler_choice $sampler_choice \
			--ckpt_dir $ckpt_dir/ckpt_$sampler_choice \
			--result_dir $result_dir/results_$sampler_choice \
			--log_dir $log_dir/logs_$sampler_choice