Search Results for author: Jie Lian

Found 7 papers, 3 papers with code

A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

1 code implementation21 Apr 2021 Jie Lian, Jingyu Liu, Shu Zhang, Kai Gao, Xiaoqing Liu, Dingwen Zhang, Yizhou Yu

Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.

Instance Segmentation Object Detection +2

ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities

1 code implementation17 Jun 2020 Jingyu Liu, Jie Lian, Yizhou Yu

Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images.

Classification General Classification

Automatically eliminating seam lines with Poisson editing in complex relative radiometric normalization mosaicking scenarios

no code implementations14 Jun 2021 Shiqi Liu, Jie Lian, Xuchen Zhan, Cong Liu, Yuze Tian, Hongwei Duan

Relative radiometric normalization (RRN) mosaicking among multiple remote sensing images is crucial for the downstream tasks, including map-making, image recognition, semantic segmentation, and change detection.

Change Detection Semantic Segmentation

Auto robust relative radiometric normalization via latent change noise modelling

no code implementations24 Nov 2021 Shiqi Liu, Lu Wang, Jie Lian, Ting Chen, Cong Liu, Xuchen Zhan, Jintao Lu, Jie Liu, Ting Wang, Dong Geng, Hongwei Duan, Yuze Tian

Relative radiometric normalization(RRN) of different satellite images of the same terrain is necessary for change detection, object classification/segmentation, and map-making tasks.

Change Detection

AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine

no code implementations24 Nov 2023 Jie Lian, Xufang Luo, Caihua Shan, Dongqi Han, Varut Vardhanabhuti, Dongsheng Li

However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources.

Physics-Inspired Degradation Models for Hyperspectral Image Fusion

no code implementations4 Feb 2024 Jie Lian, Lizhi Wang, Lin Zhu, Renwei Dian, Zhiwei Xiong, Hua Huang

To fill this gap, we propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI, which comprises a spatial degradation network (SpaDN) and a spectral degradation network (SpeDN).

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