136 citations · 295 across the 20 of their papers we have counts for
20 papers
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
Jiacheng Wang, Xiang Chen, Renjiu Hu +5
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of hum…
VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation
Dewei Hu, Hao Li, Han Liu +3
Due to the absence of a single standardized imaging protocol, domain shift between data acquired from different sites is an inherent property of medical images and has become a maj…
CATS v2: Hybrid encoders for robust medical segmentation
Hao Li, Han Liu, Dewei Hu +3
Convolutional Neural Networks (CNNs) have exhibited strong performance in medical image segmentation tasks by capturing high-level (local) information, such as edges and textures.…
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
Ruining Deng, Yanwei Li, Peize Li +11
Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires lab…
Deep Angiogram: Trivializing Retinal Vessel Segmentation
Dewei Hu, Xing Yao, Jiacheng Wang +2
Among the research efforts to segment the retinal vasculature from fundus images, deep learning models consistently achieve superior performance. However, this data-driven approach…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Self-Supervised CSF Inpainting with Synthetic Atrophy for Improved Accuracy Validation of Cortical Surface Analyses
Jiacheng Wang, Kathleen E. Larson, Ipek Oguz
Accuracy validation of cortical thickness measurement is a difficult problem due to the lack of ground truth data. To address this need, many methods have been developed to synthet…
SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI
Jiacheng Wang, Hao Li, Han Liu +6
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have sh…
Indescribable Multi-modal Spatial Evaluator
Lingke Kong, X. Sharon Qi, Qijin Shen +4
Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have di…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19
Liansheng Wang, Jiacheng Wang, Lei Zhu +6
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19. However, there are still some challenges for developing AI sy…
Cats: Complementary CNN and Transformer Encoders for Segmentation
Hao Li, Dewei Hu, Han Liu +2
Recently, deep learning methods have achieved state-of-the-art performance in many medical image segmentation tasks. Many of these are based on convolutional neural networks (CNNs)…
Personalizing Federated Medical Image Segmentation via Local Calibration
Jiacheng Wang, Yueming Jin, Liansheng Wang
Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing d…
XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
Jiacheng Wang, Fei Chen, Yuxi Ma +6
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the i…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
Jiacheng Wang, Xiaomeng Li, Yiming Han +3
Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is of great significance for radiotherapy planning. However, it is a challenging task to learn powerful re…
Boundary-aware Transformers for Skin Lesion Segmentation
Jiacheng Wang, Lan Wei, Liansheng Wang +3
Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer. However, the automatic segmentation of melanoma is a…
2020 CATARACTS Semantic Segmentation Challenge
Imanol Luengo, Maria Grammatikopoulou, Rahim Mohammadi +37
Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In…
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video
Jiacheng Wang, Yueming Jin, Liansheng Wang +3
Performing a real-time and accurate instrument segmentation from videos is of great significance for improving the performance of robotic-assisted surgery. We identify two importan…
Robust Medical Instrument Segmentation Challenge 2019
Tobias Ross, Annika Reinke, Peter M. Full +47
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tr…