Summary of Adaptive Real-time Multi-loss Function Optimization Using Dynamic Memory Fusion Framework: a Case Study on Breast Cancer Segmentation, by Amin Golnari and Mostafa Diba
Adaptive Real-Time Multi-Loss Function Optimization Using Dynamic Memory Fusion Framework: A Case Study on Breast Cancer Segmentation
by Amin Golnari, Mostafa Diba
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, dynamic memory fusion, optimizes deep learning model performance by adaptively adjusting the weighting of multiple loss functions in real-time, leveraging historical loss values data. This approach integrates an auxiliary loss function to enhance early-stage performance and addresses class imbalance using the novel class-balanced dice loss function. The framework improves segmentation performance on breast ultrasound datasets across various metrics. By dynamically prioritizing relevant criteria, the model achieves better performance in evolving environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make deep learning models work better by adjusting how they weigh different goals during training. It’s like having multiple teachers grading your assignments and you want them all to be happy with your work! The approach uses past mistakes (loss values) to adjust its focus on what’s most important, making the model more flexible and adaptable. This helps in situations where some classes or tasks are harder than others. The results show that this method works well for a specific task: segmenting images of breast tissue from ultrasound scans. |
Keywords
* Artificial intelligence * Deep learning * Loss function
