Search Results for author:
Found 9 papers, 2 papers with code
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore, which is the regions with lower density.
Retrosynthesis Prediction via Search in (Hyper) Graph
As a semi-template-based method RetroSiG has several advantages.
Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalities
Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety.
Improved statistical benchmarking of digital pathology models using pairwise frames evaluation
Nested pairwise frames is a method for relative benchmarking of cell or tissue digital pathology models against manual pathologist annotations on a set of sampled patches.
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology
Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance.
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation
Inspired by density-based unsupervised clustering, we propose to leverage feature density to locate sparse regions within feature clusters defined by label and pseudo labels.
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Our Additive MIL models enable spatial credit assignment such that the contribution of each region in the image can be exactly computed and visualized.
Sub-GMN: The Neural Subgraph Matching Network Model
Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage.
Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma
Results: The model uses an image as input and produces a 2-dimensional black and white output of prediction of the same dimension; the areas determined to be basal cell carcinoma were displayed with white color, in a black background.
