Search Results for author:
Found 10 papers, 4 papers with code
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
A parallel hierarchy of local and global feature blocks is designed to efficiently extract local features and global representations at various semantic scales, with the flexibility to model at different scales and linear computational complexity relevant to image size.
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning
In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation.
Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation
The contribution of this paper is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining.
Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining
To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining.
Multi-object Tracking with a Hierarchical Single-branch Network
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution.
Asymmetric Deep Semantic Quantization for Image Retrieval
However, we argue that the current deep learning based hashing methods ignore some critical problems (e. g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes).
Asymmetric Residual Neural Network for Accurate Human Activity Recognition
Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction.
Deep Attention-guided Hashing
The core idea is to use guided hash codes which are generated by the hashing network of the first stream framework (called first hashing network) to guide the training of the hashing network of the second stream framework (called second hashing network).
An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization
To ensure the effectiveness of the proposed LSA-based sentence selection algorithm, extensive experiment on Arabic and English are done.
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition
Using the quantization method proposed, we were able to achieve performances closer to that of full-precision counterpart.
