What deep learning strategies best balance accuracy and interpretability in medical image segmentation for disease progression analysis?
Medical imaging (CT/MRI) segmentation is vital for tracking disease, but black-box models reduce clinical trust. Methods like explainable AI (XAI), uncertainty quantification, and hybrid modeling may bridge this gap. What approaches are most promising?
3 Answers
Charles
An explainable AI sounds nonsense to me. Malignant cell recognition should be translation and rotation invariant. Moreover, the operator and evaluator bias can also distort the decision.
The only practice to be recommended:
1) balanced data set (resampling),
2) using more (>7) performance indicator; accuracy, selectivity, specificity, F1-score, etc.
3) ranking the classifiers according to these indicators, build their consensus,
4) Select the best model from the Pareto front by using multicriteria decision analysis (MCDA) by using e.g. Topsis, sum of ranking differences, VIKOR, etc.
The only practice to be recommended:
1) balanced data set (resampling),
2) using more (>7) performance indicator; accuracy, selectivity, specificity, F1-score, etc.
3) ranking the classifiers according to these indicators, build their consensus,
4) Select the best model from the Pareto front by using multicriteria decision analysis (MCDA) by using e.g. Topsis, sum of ranking differences, VIKOR, etc.
Iryna
Using hybrid architectures (e.g., modified U-Net or CNN-Transformer) to provide high segmentation accuracy.
Using XAI methods (especially Grad-CAM) to visualize where the models are focusing, allowing clinicians to track changes in lesion patterns over time.
Implementing BNNs (Bayesian neural networks) to quantify uncertainty, which can serve as an early indicator of a state change when the model becomes less "confident" in its predictions.