mndino 0.0.0.2

mn-DINO: [add description of the project later]

mn-DINO

Detecting micronuclei in images using DINOv2 backbone

Refer to tutorial notebook for real examples

Install package

pip install mndino

Load the model

import torch
from mndino import mnmodel
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="yifanren/mn-DINO", filename="latest.pth")
device = "cuda" if torch.cuda.is_available() else "cpu"
model = mnmodel.MicronucleiModel(device=device)
model.load(model_path)

Make predictions

import skimage
import numpy as np

STEP = 32 # recommended value
PREDICTION_BATCH = 4
THRESHOLD = 0.5

im = skimage.io.imread(your_image_path)
im = np.array((im - np.min(im))/(np.max(im) - np.min(im)), dtype="float32") # normalize image
probabilities = model.predict(im, stride=1, step=STEP, batch_size=PREDICTION_BATCH)

mn_predictions = probabilities[0,:,:] > THRESHOLD
nuclei_predictions = probabilities[1,:,:] > THRESHOLD

Evaluation

import skimage
from mndino import evaluation

mn_gt = skimage.io.imread(your_annotated_image_path)
evaluation.segmentation_report(imid='My_Image', predictions=mn_predictions, gt=mn_gt, intersection_ratio=0.1)

Reproducing mndino Training & Evaluation Experiments

git clone [e-mail]:CaicedoLab/micronuclei-detection.git
cd micronuclei-detection

Training

python3 training_model.py --path '/scr/data/annotated_mn_datasets/' --gpu 0 --epochs 20 --batch_size 4 --loss_fn 'combined' --lr 1e-5 --scale 1.0 --gaussian

Making Predictions on Test Set

python3 prediction.py --path '/scr/data/annotated_mn_datasets/' --test_set  --gpu 0 --step 32 --batch_size 4 --prob_threshold 0.5 --iou_threshold 0.1 --scale 1

Turn on --test_set if user wants to evaluate on test set, turn it off to select validation set.
Turn on --wandb_mode if user wants to show loss on Weights and Biases

Train your own specialist model

• Expected file extension of training images and nuclei masks is .tif, the corresponding training masks is .png. Following values are tunable if retraining on non-micronucleus subcellular datasets.
• Combined loss = 0.8 * subcellular loss + 0.2 * nuclei loss.

device = f"cuda:{gpu}" if torch.cuda.is_available() else 'cpu'
model = mnmodel.MicronucleiModel(
    device=device,
    data_dir=DIRECTORY,
    patch_size=256,
    scale_factor=1.0,
    gaussian=True,
    oversample=False # oversample option is only applied to the micronuclei dataset presented in manuscript
)

model.train(epochs=20, 
            batch_size=4, 
            learning_rate=1e-5, 
            loss_fn='combined',
            weight_decay=1e-6,
            wandb_mode=False
)

model.save(outdir=OUTPUT_DIR, model_name=MODEL_NAME)

Reproducing Baseline experiments

MNFinder Evaluation

# Run reformant mnfinder images script
python3 mnfinder_prediction.py --test_path '/scr/data/annotated_mn_datasets/test/images/' --save_path '/scr/data/annotated_mn_datasets/mnfinder_predictions/' --wandb_mode

# Retraining Cellpose3
python3 cellpose_prediction.py --gpu 0 --train_path '/scr/data/annotated_mn_datasets/train/images/' --save_path '/scr/data/annotated_mn_datasets/cellpose_predictions/' --finetune --wandb_mode

use frozen microSAM (better performance)

python3 reformat_microsam_images.py --load_path '/scr/yren/annotated_mn_datasets/' --save_path '/scr/yren/microsam_data/'

python3 microsam_prediction.py --gpu 0 --train_path '/scr/data/microsam_data/train/' --pred_path '/scr/data/annotated_mn_datasets/test/images/' --save_path '/scr/yren/microsam_data/microsam_predictions/' --frozen --wandb_mode

Turn on --frozen if user wants to use frozen microSAM backbone to make predictions