12 research outputs found
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of
predefined fiducial points. Despite recent success in this field,
higher-ordered structural modeling to capture implicit or explicit
relationships among anatomical landmarks has not been adequately exploited. In
this work, we present a new topology-adapting deep graph learning approach for
accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection.
The proposed method constructs graph signals leveraging both local image
features and global shape features. The adaptive graph topology naturally
explores and lands on task-specific structures which are learned end-to-end
with two Graph Convolutional Networks (GCNs). Extensive experiments are
conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as
well as three real-world X-ray medical datasets (Cephalometric (public), Hand
and Pelvis). Quantitative results comparing with the previous state-of-the-art
approaches across all studied datasets indicating the superior performance in
both robustness and accuracy. Qualitative visualizations of the learned graph
topologies demonstrate a physically plausible connectivity laying behind the
landmarks.Comment: Accepted to ECCV-20. Camera-ready with supplementary materia
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones
Medication for neurological diseases such as the Parkinson's disease usually
happens remotely away from hospitals. Such out-of-lab environments pose
challenges in collecting timely and accurate health status data. Individual
differences in behavioral signals collected from wearable sensors also lead to
difficulties in adopting current general machine learning analysis pipelines.
To address these challenges, we present a method for predicting the medication
status of Parkinson's disease patients using the public mPower dataset, which
contains 62,182 remote multi-modal test records collected on smartphones from
487 patients. The proposed method shows promising results in predicting three
medication statuses objectively: Before Medication (AUC=0.95), After Medication
(AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical
records with the attention weights learned through a Transformer model. Our
method provides an innovative way for personalized remote health sensing in a
timely and objective fashion which could benefit a broad range of similar
applications.Comment: Accepted to ICDH-2023. Camera ready with supplementary materia
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CANet: Context Aware Network for Brain Glioma Segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets
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CANet: Context Aware Network for Brain Glioma Segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets