Houmann Simonsen (harppizza9)

We also demonstrate extensions in a variety of settings semi-supervised learning; combining multiple scribble sources (a crowdsourcing scenario) and multi-task learning (combining scribble and mask supervision). We release expert-made scribble annotations for the ACDC dataset, and the code used for the experiments, at https//vios-s.github.io/multiscale-adversarial-attention-gates.Separating and labeling each nuclear instance (instance-aware segmentation) is the key challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been demonstrated to solve nuclear image segmentation tasks across different imaging modalities, but a systematic comparison on complex immunofluorescence images has not been performed. Deep learning based segmentation requires annotated datasets for training, but annotated fluorescence nuclear image datasets are rare and of limited size and complexity. In this work, we evaluate and compare the segmentation effectiveness of multiple deep learning architectures (U-Net, U-Net ResNet, Cellpose, Mask R-CNN, KG instance segmentation) and two conventional algorithms (Iterative h-min based watershed, Attributed relational graphs) on complex fluorescence nuclear images of various types. We propose and evaluate a novel strategy to create artificial images to extend the training set. Results show that instance-aware segmentation architectures and Cellpose outperform the U-Net architectures and conventional methods on complex images in terms of F1 scores, while the U-Net architectures achieve overall higher mean Dice scores. Training with additional artificially generated images improves recall and F1 scores for complex images, thereby leading to top F1 scores for three out of five sample preparation types. Mask R-CNN trained on artificial images achieves the overall highest F1 score on complex images of similar conditions to the training set images while Cellpose achieves the overall highest F1 score on complex images of new imaging conditions. We provide quantitative results demonstrating that images annotated by under-graduates are sufficient for training instance-aware segmentation architectures to efficiently segment complex fluorescence nuclear images.Manifold of geodesic plays an essential role in characterizing the intrinsic data geometry. However, the existing SVM methods have largely neglected the manifold structure. As such, functional degeneration may occur due to the potential polluted training. Even worse, the entire SVM model might collapse in the presence of excessive training contamination. To address these issues, this paper devises a manifold SVM method based on a novel ξ -measure geodesic, whose primary design objective is to extract and preserve the data manifold structure in the presence of training noises. To further cope with overly contaminated training data, we introduce Kullback-Leibler (KL) regularization with steerable sparsity constraint. In this way, each loss weight is adaptively obtained by obeying the prior distribution and sparse activation during model training for robust fitting. Moreover, the optimal scale for Stiefel manifold can be automatically learned to improve the model flexibility. Accordingly, extensive experiments verify and validate the superiority of the proposed method. In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV). We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating C