A5057464765- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Digital Imaging for Blood Diseases
- Image Processing Techniques and Applications
- Cardiac Imaging and Diagnostics
- Smart Agriculture and AI
- Advanced Vision and Imaging
- Advanced MRI Techniques and Applications
- Optical measurement and interference techniques
- Advanced Neural Network Applications
- Lung Cancer Diagnosis and Treatment
- Speech Recognition and Synthesis
- Brain Tumor Detection and Classification
- Plant Virus Research Studies
- Robotics and Sensor-Based Localization
- Cervical Cancer and HPV Research
- Single-cell and spatial transcriptomics
- Plant Disease Management Techniques
- Medical Image Segmentation Techniques
- Cell Image Analysis Techniques
- Cancer Genomics and Diagnostics
- Medical Imaging and Analysis
- Vehicle License Plate Recognition
- Medical Imaging Techniques and Applications
- Smart Grid and Power Systems
Sichuan University
2014-2025
Gansu Agricultural University
2023-2025
Stanford University
2024-2025
Central South University
2024
Wuhan University
2024
Tencent (China)
2020-2024
University of California, Los Angeles
2024
Xinjiang University
2023-2024
Harvard University
2024
The University of Texas Southwestern Medical Center
2023
Multiple instance learning (MIL) is a typical weakly-supervised method where the label associated with bag of instances instead single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when commonly assumed standard multiple (SMI) assumption not satisfied. In this paper, we propose based on deep graph convolutional network feature selection (FS-GCN-MIL) for histopathological image classification. The proposed...
Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency medical attention, which routinely diagnosed using non-contrast head CT imaging. The diagnostic accuracy of acute ICH on varies greatly among radiologists due to the difficulty interpreting subtle findings and time pressure associated with ever-increasing workload. use artificial intelligence technology may help automate process assist for more prompt better decision-making. In this work, we design deep...
Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal the to construct high-quality pathology learning data set that will allow greater accessibility. PAIP Liver Cancer Segmentation Challenge, organized conjunction with Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), first image analysis challenge apply datasets. was evaluate new existing algorithms for automated...
Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) non-invasive method used to evaluate coronary artery disease, as well evaluating and reconstructing heart vessel structures. Reconstructed models have wide array for educational, training research applications such the study diseased non-diseased anatomy, machine learning based risk prediction in-silico in-vitro testing medical devices. However, arteries are difficult image due their...
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology patient outcome. To drive innovation this area, we setup a community-wide challenge using largest available dataset of its kind to assess nuclear cellular composition. Our challenge, named CoNIC, stimulated development reproducible algorithms for recognition with real-time result inspection on public leaderboards. We conducted an extensive...
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task challenging for algorithms and human experts alike, with deterioration algorithmic performance under shifts image representations. Considerable covariate occur when assessment performed on different types, images are acquired using digitization devices, or produced laboratories. observation motivated the inception 2022 challenge MItosis Domain Generalization (MIDOG 2022)....
The large-scale whole-slide images (WSIs) facilitate the learning-based computational pathology methods. However, gigapixel size of WSIs makes it hard to train a conventional model directly. Current approaches typically adopt multiple-instance learning (MIL) tackle this problem. Among them, MIL combined with graph convolutional network (GCN) is significant branch, where sampled patches are regarded as nodes further discover their correlations. difficult build correspondence across from...
Purpose This paper aims to propose a real-time augmented reality (AR)-based assembly assistance system using coarse-to-fine marker-less tracking strategy. The automatically adapts requirement when the topological structure of changes after each step. Design/methodology/approach prototype system’s process can be divided into two stages: offline preparation stage and online execution stage. In stage, planning results (assembly sequence, parts position, rotation, etc.) image features [gradient...
In the clinical environment, myocardial infarction (MI) as one common cardiovascular disease is mainly evaluated using late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). Accurate segmentation of ventricles and myocardium a prerequisite for quantitative assessment functions progression. Performing task LGE is, however, rather challenging due to heterogeneous image intensity distribution lack clear boundaries between adjacent organs tissues. this paper we propose deep...
Objective.To establish an open framework for developing plan optimization models knowledge-based planning (KBP).Approach.Our includes radiotherapy treatment data (i.e. reference plans) 100 patients with head-and-neck cancer who were treated intensity-modulated radiotherapy. That also high-quality dose predictions from 19 KBP that developed by different research groups using out-of-sample during the OpenKBP Grand Challenge. The input to four fluence-based mimicking form 76 unique pipelines...
Microsatellite instability (MSI) refers to alterations in the length of simple repetitive genomic sequences. MSI status serves as a prognostic and predictive factor colorectal cancer. The MSI-high is good stage II/III cancer, predicts lack benefit adjuvant fluorouracil chemotherapy II cancer but response immunotherapy IV Therefore, determining patients with important for identifying appropriate treatment protocol. In Pathology Artificial Intelligence Platform (PAIP) 2020 challenge,...
Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cell types. Existing automated methods are primarily trained on databases covering narrow range coarse-grained types, which fail to provide comprehensive detailed performance analysis that represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center...