Publications (30)
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation
Lei Bi, Michael Fulham, Shaoli Song +2
[18F]-Fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning comple…
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…
Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation
Kai-Chieh Liang, Lei Bi, Ashnil Kumar +2
Sequential whole-body 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) scans are regarded as the imaging modality of choice for the assessment of treatment response…
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…
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…
Mixed reality hologram slicer (mxdR-HS): a marker-less tangible user interface for interactive holographic volume visualization
Hoijoon Jung, Younhyun Jung, Michael Fulham +1
Mixed reality head-mounted displays (mxdR-HMD) have the potential to visualize volumetric medical imaging data in holograms to provide a true sense of volumetric depth. An effectiv…
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…
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…
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search
Yige Peng, Lei Bi, Michael Fulham +2
'Radiomics' is a method that extracts mineable quantitative features from radiographic images. These features can then be used to determine prognosis, for example, predicting the d…
Semi-supervised estimation of event temporal length for cell event detection
Ha Tran Hong Phan, Ashnil Kumar, David Feng +2
Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods. Deep learning methods have shown great success in the detection of…
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…
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Lei Bi, Jinman Kim, Ashnil Kumar +2
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing ima…
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…
Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images
Lei Bi, Jinman Kim, Tingwei Su +3
The accurate classification of mass lesions in the adrenal glands (adrenal masses), detected with computed tomography (CT), is important for diagnosis and patient management. Adren…
Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
Lei Bi, Yuyu Guo, Qian Wang +3
Automated segmentation of the optic cup and disk on retinal fundus images is fundamental for the automated detection / analysis of glaucoma. Traditional segmentation approaches dep…
Enhancing medical vision-language contrastive learning via inter-matching relation modelling
Mingjian Li, Mingyuan Meng, Michael Fulham +3
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-t…
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…
PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network
Yuxin Xue, Lei Bi, Yige Peng +3
Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but als…
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…
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
Mingyuan Meng, Michael Fulham, Dagan Feng +2
Deformable image registration aims to find a dense non-linear spatial correspondence between a pair of images, which is a crucial step for many medical tasks such as tumor growth m…
Unsupervised Deep Transfer Feature Learning for Medical Image Classification
Euijoon Ahn, Ashnil Kumar, Dagan Feng +2
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a pau…
An unsupervised long short-term memory neural network for event detection in cell videos
Ha Tran Hong Phan, Ashnil Kumar, David Feng +2
We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events…
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…
Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention
Mingyuan Meng, Michael Fulham, Lei Bi +1
Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for…
MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction
Andrew Zhang, Hao Wang, Shuchang Ye +2
Patient motion during medical image acquisition causes blurring, ghosting, and distorts organs, which makes image interpretation challenging. Current state-of-the-art algorithms us…
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification
Euijoon Ahn, Ashnil Kumar, Dagan Feng +2
Medical image analysis using supervised deep learning methods remains problematic because of the reliance of deep learning methods on large amounts of labelled training data. Altho…
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…
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…