Continuous-Learning Neural Imaging Classification With Less Forgetting
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Solution Overview
Problem
Existing deep learning methods for clinical imaging in low and middle income countries face challenges in adapting to new data distributions over time without storing large numbers of exemplars due to memory, legal, and privacy constraints, leading to catastrophic forgetting and inefficient knowledge transfer.
Innovation Solution
Integrate Mobius transformations for input space augmentation and weighted cross-distillation in a distillation-driven continual learning procedure to maintain past knowledge while learning new classes, using a limited number of samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning models are retrained incrementally with new data to adapt to evolving data distributions, then model adaptability improves, but catastrophic forgetting of past knowledge occurs
Solution Approach 1:
The system performs preliminary actions by storing a limited set of exemplars from past classes before incremental retraining occurs. These stored exemplars serve as a memory buffer that prevents catastrophic forgetting during subsequent learning of new classes, allowing the model to adapt while retaining past knowledge.
Solution Approach 2:
The system introduces an intermediary mechanism (exemplar storage buffer) that mediates between old and new knowledge. This buffer acts as a bridge, allowing the model to learn new classes while periodically reviewing past exemplars to maintain performance on previously learned classes.
2Reliability
If large numbers of exemplars from past classes are stored to prevent forgetting, then model robustness improves, but memory storage requirements increase
Solution Approach 1:
Instead of storing all possible exemplars from past classes, the system applies partial action by selecting and storing only a limited subset of critical exemplars. This selective storage approach maintains model robustness while significantly reducing memory requirements compared to storing complete datasets.
Solution Approach 2:
The system changes the parameter of exemplar quantity from storing large numbers of samples to storing a limited, optimized set. This parameter change is achieved through selective exemplar retention strategies that identify and store only the most informative samples needed to prevent catastrophic forgetting.
3Productivity
If transfer learning is used to adapt models to new classes, then learning efficiency improves, but imbalance between old and new task knowledge occurs
Solution Approach 1:
The system applies periodic action by implementing a cyclical review process where the model periodically revisits stored exemplars from past classes during incremental learning. This periodic reinforcement ensures that performance on old classes is maintained while learning new classes, creating a balanced knowledge composition.
Solution Approach 2:
The system uses feedback mechanisms to monitor and balance performance across old and new classes. By tracking validation performance on both previously learned and newly introduced classes, the system adjusts its learning process to maintain equilibrium, preventing dominance by either old or new task knowledge.
Data Source
AI summary
A computer-implemented method for improving classification performance of a neural network module that has been pre-trained on a set of imaging data includes (a) applying Mobius data augmentation to one or more imaging data from a data set that have been already used to train the neural network module, said imaging data having a classification label assigned for each image, and storing a resulting transformed imaging data; (b) receiving a new imaging data set, said set comprising data for a set of images that have a classification label assigned; and (c) updating the neural network module by training the neural network on a combination of the imaging data obtained in step (a) and steps (b) and storing the resulting neural network module.