215 citations · 345 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…