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Found 10 papers, 3 papers with code
NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative Learning
To address this, we introduce two cascading modules based on the barycenter of the mask, which are Coordinate Guided Aggregation (CGA) and Barycenter Driven Localization (BDL), responsible for segmentation and detection, respectively.
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers.
Rotation-Scale Equivariant Steerable Filters
When conventional CNNs are applied to histopathology image analysis, the generalization performance of models is limited because 1) a part of the parameters of filters are trained to fit rotation transformation, thus decreasing the capability of learning other discriminative features; 2) fixed-size filters trained on images at a given scale fail to generalize to those at different scales.
Scale-Equivariant UNet for Histopathology Image Segmentation
Digital histopathology slides are scanned and viewed under different magnifications and stored as images at different resolutions.
ADS_UNet: A Nested UNet for Histopathology Image Segmentation
Nested arrangements of these encoder and decoder maps give rise to extensions of the UNet model, such as UNete and UNet++.
Cloud-based Federated Boosting for Mobile Crowdsensing
In this paper, we propose a secret sharing based federated learning architecture FedXGB to achieve the privacy-preserving extreme gradient boosting for mobile crowdsensing.
An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction
Conclusions: The integrated transfer learning and multitask learning approach with the improved dataset splitting algorithm was firstly introduced to predict the pharmacokinetic parameters.
Deep learning for in vitro prediction of pharmaceutical formulations
In summary, deep learning with the automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was firstly developed for the prediction of pharmaceutical formulations.
ServeNet: A Deep Neural Network for Web Services Classification
Automated service classification plays a crucial role in service discovery, selection, and composition.
Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques
ANN and DNN were compared for the prediction of the disintegrating time.
