Search Results for author:
Found 10 papers, 4 papers with code
Towards Comprehensive Vietnamese Retrieval-Augmented Generation and Large Language Models
This paper presents our contributions towards advancing the state of Vietnamese language understanding and generation through the development and dissemination of open datasets and pre-trained models for Vietnamese Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).
UGCANet: A Unified Global Context-Aware Transformer-based Network with Feature Alignment for Endoscopic Image Analysis
The early detection and identification of lesions in the upper gastrointestinal tract and the identification of malignant polyps that may pose a risk of cancer development are critical components of gastrointestinal endoscopy's diagnostic and therapeutic applications.
Ranked #3 on
Medical Image Segmentation
on CVC-ClinicDB
RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation
Automatic and accurate segmentation of colon polyps is essential for early diagnosis of colorectal cancer.
Online pseudo labeling for polyp segmentation with momentum networks
In this work, we present a new pseudo labeling strategy that enhances the quality of pseudo labels used for training student networks.
A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment
Domain adaptation methods in machine learning deal with the domain shift issue by aligning source and target data representation.
Ranked #12 on
Unsupervised Domain Adaptation
on SYNTHIA-to-Cityscapes
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a source dataset with dense pixel-level annotations to an unlabeled target dataset.
Ranked #8 on
Semantic Segmentation
on GTAV-to-Cityscapes Labels
ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation
The encoder is a lightweight architecture based on transformers for modeling global semantic relations at multi scales.
Ranked #7 on
Medical Image Segmentation
on CVC-ClinicDB
BlazeNeo: Blazing fast polyp segmentation and neoplasm detection
In recent years, computer-aided automatic polyp segmentation and neoplasm detection have been an emerging topic in medical image analysis, providing valuable support to colonoscopy procedures.
NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection
Automatic polyp segmentation has proven to be immensely helpful for endoscopy procedures, reducing the missing rate of adenoma detection for endoscopists while increasing efficiency.
AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation
Colorectal cancer is among the most common malignancies and can develop from high-risk colon polyps.
Ranked #25 on
Medical Image Segmentation
on CVC-ClinicDB
