activity
20202023
most citedImproving Chest X-Ray Report Generation by Leveraging Warm Starting

159 citations · 162 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 1 cited

Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation

Aaron Nicolson, Jason Dowling, Bevan Koopman

Radiologists face high burnout rates, partially due to the increasing volume of Chest X-rays (CXRs) requiring interpretation and reporting. Automated CXR report generation holds pr…

cs.CV2022★ 159 cited

Improving Chest X-Ray Report Generation by Leveraging Warm Starting

Aaron Nicolson, Jason Dowling, Bevan Koopman

Automatically generating a report from a patient's Chest X-Rays (CXRs) is a promising solution to reducing clinical workload and improving patient care. However, current CXR report…

eess.IV2021

CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation

Wei Dai, Boyeong Woo, Siyu Liu +6

Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of in…

cs.CV2020★ 2 cited

Manipulating Medical Image Translation with Manifold Disentanglement

Siyu Liu, Jason A. Dowling, Craig Engstrom +3

Medical image translation (e.g. CT to MR) is a challenging task as it requires I) faithful translation of domain-invariant features (e.g. shape information of anatomical structures…

eess.IV2020

Going deeper with brain morphometry using neural networks

Rodrigo Santa Cruz, Léo Lebrat, Pierrick Bourgeat +5

Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convoluti…

eess.IV2020

Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation

Siyu Liu, Wei Dai, Craig Engstrom +4

Data scarcity is common in deep learning models for medical image segmentation. Previous works proposed multi-dataset learning, either simultaneously or via transfer learning to ex…