Finding Lymphocytes

This repository represent the code usedduring the experiments for my master thesis.

Counting lymphocytes with density maps

The idea is to phrase the tassk of counting lymphocytes as the a segmentation task where we try to reproduce a particular kind of target: a density maps.

Density map object count and localization

Dens map single gpu

python dens_count.py
-g 0 # gpu to use, gpu id -f False # freeze encoder layer -r /home/papa/ly_decount/C_count_dens_map/experiments/dens_count_se_resnet50_imagenet_ep_120_bs_16_2020-11-12T13:18:13.581819/last.pth # optional, if we want to resume from checkpoint
-e se_resnet50 # model encoder architecture (see pytorch segmentation models)

-e : ["resnet50", "se_resnet50",

Dens map distributed

launch experiment with efficient b0 on 2 gpu

python dens_count_parallel.py -g 0,1 -en efficientnet-b0 -f True -s 6 -bs 52

experiment with efficientnet b4 on 2 gpu

python dens_count_parallel.py -g 2,3 -en efficientnet-b4 -f True -s 6 -bs 52

python dens_count_parallel.py -g 0,1 -en efficientnet-b3 -f True -s 5 -bs 32

efficientnet b3 full open

python dens_count_parallel.py -g 2,3 -en efficientnet-b3 -f False -s 5 -bs 16 -r /home/papa/ly_decount/C_count_dens_map/experiments/dens_count_efficientnet-b3_imagenet_ep_240_bs_18_resume_2020-11-17T02:39:07.243330/last.pth -lre 1.0

efficientnet b3 frozen from imagenet and lower sigma and lr scheduler

python dens_count_parallel.py -g 2,3 -en efficientnet-b3 -f True -s 4 -bs 32

python dens_count_parallel.py -f True -lrf 1e-2 -g 2,3 -en efficientnet-b3 -s 5 -bs 32 -o ranger -nt efficientnetb3_from_scratch_with_plateau_scheduler_and_rangerlars_as_optimizer

heavy augmentation

python dens_count_parallel.py -g 0,1 -en efficientnet-b3 -f False -s 5 -bs 12 -lr 1e-2 -lre 0.1 -lrf 0.5 -sh 0.5 -nt try_with_heav_color_augmentation_lr_scheduler_and_high_lr_start