7 citations · 19 across the 5 of their papers we have counts for
5 papers
HRDecoder: High-Resolution Decoder Network for Fundus Image Lesion Segmentation
Ziyuan Ding, Yixiong Liang, Shichao Kan +1
High resolution is crucial for precise segmentation in fundus images, yet handling high-resolution inputs incurs considerable GPU memory costs, with diminishing performance gains a…
Exploring Contextual Relationships for Cervical Abnormal Cell Detection
Yixiong Liang, Shuo Feng, Qing Liu +5
Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell…
M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images
Qing Liu, Haotian Liu, Wei Ke +1
Feature reassembly is an essential component in modern CNN-based segmentation approaches, which includes feature downsampling and upsampling operators. Existing operators reassembl…
Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation from Colour Fundus Images
Qing Liu, Haotian Liu, Yixiong Liang
Automated segmentation of hard exudates in colour fundus images is a challenge task due to issues of extreme class imbalance and enormous size variation. This paper aims to tackle…
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
Yixiong Liang, Zhihong Tang, Meng Yan +3
Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, mo…