Search Results for author:
Found 5 papers, 0 papers with code
FourierLoss: Shape-Aware Loss Function with Fourier Descriptors
Different than the previous studies, FourierLoss offers an adaptive loss function with trainable hyperparameters that control the importance of the level of the shape details that the network is enforced to learn in the training process.
Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images
Different from the previously suggested segmentation network designs, which apply the threshold filtration on a likelihood function of the prediction map and the Betti numbers of the ground truth, this paper proposes to apply the Vietoris-Rips filtration to obtain persistence diagrams of both ground truth and prediction maps and calculate the dissimilarity with the Wasserstein distance between the corresponding persistence diagrams.
FourierNet: Shape-Preserving Network for Henle's Fiber Layer Segmentation in Optical Coherence Tomography Images
This paper addresses this issue by introducing a shape-preserving network, FourierNet, that achieves HFL segmentation in standard OCT scans with the target performance obtained when directional OCT scans are used.
DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images
For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model.
AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks
AttentionBoost designs a multi-stage network and introduces a new loss adjustment mechanism for a dense prediction model to adaptively learn what to attend at each stage directly on image data without necessitating any prior definition about what to attend.
