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Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions.
- Source :
- IEEE Transactions on Medical Imaging; Feb2021, Vol. 40 Issue 2, p585-593, 9p
- Publication Year :
- 2021
-
Abstract
- Deep learning is becoming an indispensable tool for various tasks in science and engineering. A critical step in constructing a reliable deep learning model is the selection of a loss function, which measures the discrepancy between the network prediction and the ground truth. While a variety of loss functions have been proposed in the literature, a truly optimal loss function that maximally utilizes the capacity of neural networks for deep learning-based decision-making has yet to be established. Here, we devise a generalized loss function with functional parameters determined adaptively during model training to provide a versatile framework for optimal neural network-based decision-making in small target segmentation. The method is showcased by more accurate detection and segmentation of lung and liver cancer tumors as compared with the current state-of-the-art. The proposed formalism opens new opportunities for numerous practical applications such as disease diagnosis, treatment planning, and prognosis. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL neural networks
COST functions
DECISION making
DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 02780062
- Volume :
- 40
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Medical Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 148595820
- Full Text :
- https://doi.org/10.1109/TMI.2020.3031913