238 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of SydneyAU19 papers
- Shanghai Jiao Tong UniversityCN7 papers
- Aichi Medical UniversityJP2 papers
- Bostwick LaboratoriesUS2 papers
- Cleveland ClinicUS2 papers
- Indiana University School of Medicine2 papers
- Jikei University School of MedicineJP2 papers
- Karolinska InstitutetSE2 papers
- Medical College of WisconsinUS2 papers
- Queen Mary University of LondonGB2 papers
- Southmead HospitalGB2 papers
- Taipei Veterans General HospitalTW2 papers
20 papers
Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States
Tyra Girdwood, Saba Kheirinejad, Parnian Kheirkhah Rahimabad +4
For patients experiencing cancer, nurse navigation can ease the burden of complex care by enhancing coordination of health services and patient outcomes. However, in under-resource…
Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions
Lei Bi, Michael Fulham, Jinman Kim
Automatic skin lesion segmentation methods based on fully convolutional networks (FCNs) are regarded as the state-of-the-art for accuracy. When there are, however, insufficient tra…
Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments
Mingjian Li, Mingyuan Meng, Shuchang Ye +4
Medical image segmentation is a fundamental task in numerous medical engineering applications. Recently, language-guided segmentation has shown promise in medical scenarios where t…
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
Mingyuan Meng, Lei Bi, Michael Fulham +2
Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and med…
Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
Mingyuan Meng, Lei Bi, Michael Fulham +2
Image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to p…
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
Mingyuan Meng, Bingxin Gu, Michael Fulham +4
Survival prediction is a major concern for cancer management. Deep survival models based on deep learning have been widely adopted to perform end-to-end survival prediction from me…
Random Forests for time-fixed and time-dependent predictors: The DynForest R package
Anthony Devaux, Cécile Proust-Lima, Robin Genuer
The R package DynForest implements random forests for predicting a continuous, a categorical or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent pre…
Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning
Yige Peng, Lei Bi, Ashnil Kumar +3
Distant metastases (DM) refer to the dissemination of tumors, usually, beyond the organ where the tumor originated. They are the leading cause of death in patients with soft-tissue…
Continental generalization of an AI system for clinical seizure recognition
Yikai Yang, Nhan Duy Truong, Christina Maher +2
Electroencephalogram (EEG) monitoring and objective seizure identification is an essential clinical investigation for some patients with epilepsy. Accurate annotation is done throu…
Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images
Xiaohang Fu, Lei Bi, Ashnil Kumar +2
The identification of melanoma involves an integrated analysis of skin lesion images acquired using the clinical and dermoscopy modalities. Dermoscopic images provide a detailed vi…
Enhancing Medical Image Registration via Appearance Adjustment Networks
Mingyuan Meng, Lei Bi, Michael Fulham +2
Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variati…
Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT
Xiaohang Fu, Lei Bi, Ashnil Kumar +2
Accurate characterisation of visual attributes such as spiculation, lobulation, and calcification of lung nodules is critical in cancer management. The characterisation of these at…
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation
Xiaohang Fu, Lei Bi, Ashnil Kumar +2
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection with PE…
Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
Wouter Bulten, Maschenka Balkenhol, Jean-Joël Awoumou Belinga +17
While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems…
Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models
Tennison Liu, Nhan Duy Truong, Armin Nikpour +2
Epilepsy affects nearly 1% of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for ep…
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
Peter Ström, Kimmo Kartasalo, Henrik Olsson +29
Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-…
Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer
Ashnil Kumar, Michael Fulham, Dagan Feng +1
The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications requires combining the sensitivity of…
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
Haozhe Jia, Yang Song, Donghao Zhang +5
Advanced deep learning methods have been developed to conduct prostate MR volume segmentation in either a 2D or 3D fully convolutional manner. However, 2D methods tend to have limi…
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
Euijoon Ahn, Jinman Kim, Ashnil Kumar +2
The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features t…
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
Siqi Liu, Sidong Liu, Sonia Pujol +4
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morpho…