Skip to content
/ HPC_DPAI Public

The code used in the manuscript, "Scaling the Inference of Digital Pathology Deep Learning Models using CPU-based High-Performance Computing."

License

Notifications You must be signed in to change notification settings

DIDSR/HPC_DPAI

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

1 Image patch extraction
2 Prediction
3 Heatmap stitching
4 Retreiving run-time statistics for prediction
5 Retreiving wall-clock time statistics for extraction and grouping

1 Image patch extraction

  • Adjust configuration parameters in files config_testing.txt, config_normal.txt and config_tumor.txt located at the root directory of the codes.
  • Run the commands listed in the following subsections to launch Son of Grid Engine (SGE) jobs to extract, group patches in HDF5 files and create a lookup table for every HDF5 file.

1.1 Extract and group

  • qsub ./image_patch_extract/split_main.sh ./config_testing.txt
    -- split_main.sh in turn submits split_grp.sh which in turns runs split_grp.py in array job fashion. Every task in the array job processes one slide.
  • qsub ./image_patch_extract/split_main.sh ./config_normal.txt
  • qsub ./image_patch_extract/split_main.sh ./config_tumor.txt

The *.sh files mentioned in this section are located under image_patch_extract directory while the config_*.txt files are at the root directory of the codes.

1.2 Create lookup tables

  • bash ./image_patch_extract/create_lookup_grp.sh ./config_testing.txt
  • bash ./image_patch_extract/create_lookup_grp.sh ./config_normal.txt
  • bash ./image_patch_extract/create_lookup_grp.sh ./config_tumor.txt

The lookup tables are created only once and used at Prediction stage for launching array job tasks. These tasks are run in parallel and scalable manner - if there are not enough resourcs for running all tasks then they are queued up automatically and started as resources become available. Each task processes only one group.

The *.sh file mentioned in this section is located under image_patch_extract directory while the config_*.txt files are located at the root directory of the codes.

2 Prediction

  • Additionally adjust configuration parameters in files, config_testing_cn_true.txt, config_normal_cn_true.txt, config_tumor_cn_true.txt located at at the root directory of the codes.
  • Run commands listed in the below subsections to launch SGE jobs to generate prediction matrices.

The *.sh files mentioned in sections 2.1 and 2.2 below are located under prediction directory while the config_*.txt files are at the root directory of the codes.

2.1 With color normalization

  • qsub ./prediction/process_main.sh ./config_testing_cn_true.txt
    -- process_main.sh in turn submits a number of SGE jobs using process_array.sh which in turn runs process_images_grp_normalization_wli.py in the array jobs generated for every slide. Number of tasks in an array job determined automatically based on the number of groups in the corresponding HDF5 file.
  • qsub ./prediction/process_main.sh ./config_normal_cn_true.txt
  • qsub ./prediction/process_main.sh ./config_tumor_cn_true.txt

2.2 Without color normalization

  • qsub ./prediction/process_main.sh ./config_testing.txt
  • qsub ./prediction/process_main.sh ./config_normal.txt
  • qsub ./prediction/process_main.sh ./config_tumor.txt

3 Heatmap stitching

After the predictions matrices have been generated an SGE job using heatmap_main.sh SGE scrip could be launched to genertae heatmaps. Two arguments for this launch are: a) type of the slides (test, normal or tumor); b) the root directory of the results, like in below example run:

  • qsub ./heatmap_stitch/heatmap_main.sh test results_directory
    -- heatmap_main.sh in turn calls heatmap_arr.sh which runs heatmap_assembly.py for the heatmap stitching of all slides in parallel/scalable manner.

The files mentioned in this section are located under heatmap_stitch directory.

4 Retreiving run-time statistics for prediction

4.1 CPU time

In time_all_stats_pred.sh file adjust job results root directory, DIR and slides type, PREFIX (normal, test or tumor), like below:

  • DIR=results_directory
  • PREFIX=normal
    Then run:
  • time bash ./time_all_stats_pred.sh

4.2 Wall-Clock time

In wall_clock_time_stats_pred.sh file adjust job results root directory, DIR and slides type, PREFIX (normal, test or tumor), like below:

  • DIR=results_directory
  • PREFIX=test
    Then run:
  • time bash ./wall_clock_time_stats_pred.sh

5 Retreiving wall-clock time statistics for extraction and grouping

Run time_stats_sg_V2.sh Linux scrip with two arguments: a) type of slides (test, normal or tumor) and b) the root directory of the results:

  • time bash time_stats_sg_V2.sh [test | normal | tumor] DIR

An example run:

  • time bash wall_clock_time_split_group_V2.sh test results_directory

About

The code used in the manuscript, "Scaling the Inference of Digital Pathology Deep Learning Models using CPU-based High-Performance Computing."

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published