The task of Human-Object Interaction (HOI) detection is to detect humans...
Medical image analysis using deep learning is often challenged by limite...
The Contrastive Language-Image Pre-training (CLIP) has recently shown
re...
Finding abnormal lymph nodes in radiological images is highly important ...
Estimating displacement vector field via a cost volume computed in the
f...
Liver tumor segmentation and classification are important tasks in compu...
Radiotherapists require accurate registration of MR/CT images to effecti...
Liver cancer has high morbidity and mortality rates in the world. Multi-...
Real-world medical image segmentation has tremendous long-tailed complex...
Object detection is a fundamental task in computer vision, which has bee...
Self-supervised learning (SSL) has recently achieved promising performan...
Deep learning empowers the mainstream medical image segmentation methods...
As few-shot object detectors are often trained with abundant base sample...
Human readers or radiologists routinely perform full-body multi-organ
mu...
Automatic parsing of human anatomies at instance-level from 3D computed
...
Accurate and robust abdominal multi-organ segmentation from CT imaging o...
Human pose estimation (HPE) usually requires large-scale training data t...
Object detection under imperfect data receives great attention recently....
Modern object detectors have achieved impressive progress under the clos...
Semi-supervised object detection (SSOD) has achieved substantial progres...
Background Aims: Hepatic steatosis is a major cause of chronic liver...
The simultaneous recognition of multiple objects in one image remains a
...
In this work, we introduce a fast and accurate method for unsupervised 3...
The crucial problem in vehicle re-identification is to find the same veh...
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1...
Nowadays, Graph Neural Networks (GNNs) following the Message Passing par...
Measuring lesion size is an important step to assess tumor growth and mo...
Accurately segmenting a variety of clinically significant lesions from w...
Depending on the application, radiological diagnoses can be associated w...
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich
...
Conventional Supervised Learning approaches focus on the mapping from in...
Monitoring treatment response in longitudinal studies plays an important...
Radiological images such as computed tomography (CT) and X-rays render
a...
Large-scale datasets with high-quality labels are desired for training
a...
Identifying, measuring and reporting lesions accurately and comprehensiv...
Determining the spread of GTV_LN is essential in defining the respective...
Finding, identifying and segmenting suspicious cancer metastasized lymph...
Most current pipelines for spatio-temporal action localization connect
f...
Ultrasound (US) is a critical modality for diagnosing liver fibrosis.
Un...
In clinical trials, one of the radiologists' routine work is to measure ...
Effective and non-invasive radiological imaging based tumor/lesion
chara...
Lesion detection is an important problem within medical imaging analysis...
Finding and identifying scatteredly-distributed, small, and critically
i...
Acquiring large-scale medical image data, necessary for training machine...
When reading medical images such as a computed tomography (CT) scan,
rad...
In radiology, radiologists not only detect lesions from the medical imag...
In radiologists' routine work, one major task is to read a medical image...
In radiologists' routine work, one major task is to read a medical image...
Automatic lesion detection from computed tomography (CT) scans is an
imp...
Person re-identification (ReID) is a challenging task due to arbitrary h...