Search Results for author:
Found 16 papers, 12 papers with code
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Modality Bank: Learn multi-modality images across data centers without sharing medical data
Multi-modality images have been widely used and provide comprehensive information for medical image analysis.
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encoding
The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver fibrosis stage, which are usually obtained from liver biopsy.
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.
Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy.
Enhanced MRI Reconstruction Network using Neural Architecture Search
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures.
Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task.
Ranked #11 on
Oriented Object Detection
on DOTA 1.0
Learn distributed GAN with Temporary Discriminators
Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators?
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).
Vertebra-Focused Landmark Detection for Scoliosis Assessment
The comparison results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
In this paper, we propose a new box-based cell instance segmentation method.
MRI Reconstruction via Cascaded Channel-wise Attention Network
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.
