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Name: BDAV Research Group

Type: Organization

Bio: Biomedical Data Analysis and Visualisation (BDAV) Lab

Location: Sydney

BDAV Research Group's Projects

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3D-AEGAN: adaptive residual estimation-based 3D generative adversarial network for standard-dose image quality recovery from low-dose PET images

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An Appearance Adjustment Network (AAN) to enhance deep learning-based image registration.

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[MICCAI2022] A Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable image registration.

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Official PyTorch implementation of AAAI-22: Self-supervised Representation Learning Framework for Remote PhysiologicalMeasurement using Spatiotemporal Augmentation Loss (SLF-RPM)

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Automated Skin Lesion Segmentation via Image-wise Supervised Learning and Multi-scale Superpixel Based Cellular Automata

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A 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation.

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[MICCAI2023] XSurv: Merging-Diverging Hybrid Transformer Networks for Survival Prediction

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