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Found 7 papers, 3 papers with code
Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation
Numerous methods have been developed to improve the robustness of machine learning methods to stain variation, but comparative studies have demonstrated limited benefits to performance.
Focal Attention Networks: optimising attention for biomedical image segmentation
In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation.
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation
Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks.
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation
However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice.
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0. 878 and mean IoU of 0. 809, a 14% and 15% improvement over the previous state-of-the-art results of 0. 768 and 0. 702, respectively.
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust to class imbalance and consistently outperforms the other loss functions.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
