MRI Multitask Learning (MTL)#
In MTL reconstruction & segmentation, the segmentation task can be performed either in an multiclass or a multilabel manner. The segmentation_mode needs to be set either to multiclass or multilabel to ensure the chosen configurations aligns with the segmentation approach. Make sure to add the background_class when multiclass is selected.
Multiclass vs. Multilabel Segmentation#
Feature |
Multiclass |
Multilabel |
|---|---|---|
Pixel Constraint |
Each class is dependent
(Only one class can be assigned per voxel)
|
Each class is independent
(Multiple classes can be assigned per voxel)
|
Channel Output (\(C\)) |
\(N + 1\)
(Includes explicit Background class)
|
\(N\)
(Background is implicit)
|
Activation Function |
Softmax
\(\frac{e^{z_i}}{\sum e^{z_j}}\) (Coupled probabilities)
|
Sigmoid
\(\frac{1}{1 + e^{-z_i}}\) (Independent probabilities)
|
Background Logic |
Explicit Class
\(P(BG) = 1 - \sum P(Foreground)\)
|
Implicit Absence
All channels \(\approx 0\)
|
Inference Decision |
Argmax
(Select index with highest probability)
|
Thresholding
(Select all indices where \(P > 0.5\))
|
Target Label Format |
Flat Integer Map \((H, W)\)
(or One-Hot)
|
Stacked Binary Masks \((H, W, N)\)
|
Loss Function |
Categorical Cross-Entropy
(Penalizes the target class vs. all others)
|
Binary Cross-Entropy
(Penalizes each channel independently)
|
Probability Space |
Joint Distribution (Sum = 1) |
Independent Bernoulli Distributions |