14 citations · 62 across the 29 of their papers we have counts for
5 papers · 1 filter
TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction
Linfeng Liu, Junyan Lyu, Siyu Liu +3
The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately pr…
Evidence-aware multi-modal data fusion and its application to total knee replacement prediction
Xinwen Liu, Jing Wang, S. Kevin Zhou +2
Deep neural networks have been widely studied for predicting a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imag…
Explainable Semantic Medical Image Segmentation with Style
Wei Dai, Siyu Liu, Craig B. Engstrom +1
Semantic medical image segmentation using deep learning has recently achieved high accuracy, making it appealing to clinical problems such as radiation therapy. However, the lack o…
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision
Siyuan Yan, Zhen Yu, Xuelin Zhang +5
Deep neural networks have demonstrated promising performance on image recognition tasks. However, they may heavily rely on confounding factors, using irrelevant artifacts or bias w…
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI
Marlon E. Bran Lorenzana, Shekhar S. Chandra, Feng Liu
Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sens…