215 citations · 345 across the 14 of their papers we have counts for
14 papers
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
Xian Wu, Shuxin Yang, Zhaopeng Qiu +6
Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important…
Learning Incrementally to Segment Multiple Organs in a CT Image
Pengbo Liu, Xia Wang, Mengsi Fan +9
There exists a large number of datasets for organ segmentation, which are partially annotated and sequentially constructed. A typical dataset is constructed at a certain time by cu…
Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment
Shuxin Yang, Xian Wu, Shen Ge +2
In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an autom…
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge
Shuxin Yang, Xian Wu, Shen Ge +2
Automatic radiology report generation is critical in clinics which can relieve experienced radiologists from the heavy workload and remind inexperienced radiologists of misdiagnosi…
Pathological Image Segmentation with Noisy Labels
Li Xiao, Yinhao Li, Luxi Qv +3
Segmentation of pathological images is essential for accurate disease diagnosis. The quality of manual labels plays a critical role in segmentation accuracy; yet, in practice, the…
One-Shot Medical Landmark Detection
Qingsong Yao, Quan Quan, Li Xiao +1
The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medica…
Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets
Pengbo Liu, Li Xiao, S. Kevin Zhou
There exists a large number of datasets for organ segmentation, which are partially annotated, and sequentially constructed. A typical dataset is constructed at a certain time by c…
You Only Learn Once: Universal Anatomical Landmark Detection
Heqin Zhu, Qingsong Yao, Li Xiao +1
Detecting anatomical landmarks in medical images plays an essential role in understanding the anatomy and planning automated processing. In recent years, a variety of deep neural n…
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Pengbo Liu, Hu Han, Yuanqi Du +9
Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segme…
G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object Detection
Yufan Luo, Li Xiao
Multi-task learning is widely used in computer vision. Currently, object detection models utilize shared feature map to complete classification and localization tasks simultaneousl…
Label-Free Segmentation of COVID-19 Lesions in Lung CT
Qingsong Yao, Li Xiao, Peihang Liu +1
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herei…
PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy
Li Xiao, Yufan Luo, Chunlong Luo +5
Many recently developed object detectors focused on coarse-to-fine framework which contains several stages that classify and regress proposals from coarse-grain to fine-grain, and…
Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography
Li Xiao, Cheng Zhu, Junjun Liu +3
Deep learning object detection algorithm has been widely used in medical image analysis. Currently all the object detection tasks are based on the data annotated with object classe…
DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
Li Xiao, Chunlong Luo, Tianqi Yu +6
Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2,…