Detnet: Deep Neural Network For Particle Detection In Fluorescence Microscopy Images
1 minute read
What is this post about?
- Notes from reading the 2019 paper
Short take
- Hourglass-shaped network for detection of spots/foci/particles in cells
- Based on Deconvolution network with the following changes
- Reduced number of feature maps
- Reduces size of receptive field (two instead of five poolings)
- No long-range skip connections (detailed boundary information is irrelevant)
- Use residual blocks instead of convolution (to reduce issue of vanishing gradient)
- Use instance- instead of batch-normalisation (because little training data)
- Use bilinear upsampling instead of transposed convolutions (expanding path)
- 17k parameters instead of U-net’s 1.9M
- Use soft Dice loss (performs implicit class balancing and penalizes easy samples)
- Use early stopping
- Availability
- No information given, no links, not GitHub names
- Contact with first-author points to company in Berlin
Miscellaneous
- Details
- AMSGrad optimizer
- in-house hyperparameter optimization framework HyperHyper
- Quotes
- domain adapted Deconvolution Network
- can be trained with only a few ground truth annotations
- benchmarked using data from the ISBI Particle Tracking Challenge
- including particles with different shapes (round and elongated)
- when using a standard Cross-Entropy loss, training was not successful due to the heavy class imbalance
- the F1 score can be significantly improved by optimizing the shift a ∈ R of the sigmoid function of the neural network
- The assignment between particle detections and ground truth was determined by the Munkres algorithm with a maximal gating distance of 5 pixels.