Search Results for author: Shunxing Bao

Found 19 papers, 7 papers with code

Compound Figure Separation of Biomedical Images with Side Loss

1 code implementation19 Jul 2021 Tianyuan Yao, Chang Qu, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Catie Chang, Haichun Yang, Yuankai Huo

Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations.

Contrastive Learning Image Augmentation +1

Attention-Guided Supervised Contrastive Learning for Semantic Segmentation

no code implementations3 Jun 2021 Ho Hin Lee, Yucheng Tang, Qi Yang, Xin Yu, Shunxing Bao, Bennett A. Landman, Yuankai Huo

In this paper, we propose an attention-guided supervised contrastive learning approach to highlight a single semantic object every time as the target.

Contrastive Learning Image Classification +1

RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior

1 code implementation23 Dec 2020 Ho Hin Lee, Yucheng Tang, Shunxing Bao, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model.

CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns

no code implementations2 Nov 2020 Ruining Deng, Quan Liu, Shunxing Bao, Aadarsh Jha, Catie Chang, Bryan A. Millis, Matthew J. Tyska, Yuankai Huo

This paper makes the following contributions: (1) we approach the weakly supervised segmentation from a novel codebook learning perspective; (2) the CaCL algorithm segments diffuse image patterns rather than focal objects; and (3) The proposed algorithm is implemented in a multi-task framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) to perform image reconstruction, classification, feature embedding, and segmentation.

Image Reconstruction Weakly supervised segmentation

Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks

no code implementations3 Apr 2019 Anirban Bhattacharjee, Yogesh Barve, Shweta Khare, Shunxing Bao, Aniruddha Gokhale, Thomas Damiano

With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task.

Edge-computing

3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles

2 code implementations28 Mar 2019 Yuankai Huo, Zhoubing Xu, Yunxi Xiong, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman

To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.

Brain Segmentation

Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics

no code implementations21 Feb 2019 Jiachen Wang, Riqiang Gao, Yuankai Huo, Shunxing Bao, Yunxi Xiong, Sanja L. Antic, Travis J. Osterman, Pierre P. Massion, Bennett A. Landman

The results show that the AUC obtained from clinical demographics alone was 0. 635 while the attention network alone reached an accuracy of 0. 687.

Computed Tomography (CT)

Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network

no code implementations10 Dec 2018 Cailey I. Kerley, Yuankai Huo, Shikha Chaganti, Shunxing Bao, Mayur B. Patel, Bennett A. Landman

For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images.

Computed Tomography (CT) Medical Image Retrieval

Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks

no code implementations9 Nov 2018 Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Hyeonsoo Moon, Prasanna Parvathaneni, Tamara K. Moyo, Michael R. Savona, Albert Assad, Richard G. Abramson, Bennett A. Landman

A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network.

Splenomegaly Segmentation On Multi-Modal Mri

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

1 code implementation15 Oct 2018 Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K. Moyo, Michael R. Savona, Richard G. Abramson, Bennett A. Landman

SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality.

Semantic Segmentation

Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth

1 code implementation20 Dec 2017 Yuankai Huo, Zhoubing Xu, Shunxing Bao, Albert Assad, Richard G. Abramson, Bennett A. Landman

Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT.

Image-to-Image Translation Medical Image Segmentation +1

Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks

1 code implementation2 Dec 2017 Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Andrew J. Plassard, Jiaqi Liu, Yuang Yao, Albert Assad, Richard G. Abramson, Bennett A. Landman

However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.

Semantic Segmentation

Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation

no code implementations2 Dec 2017 Yuankai Huo, Shunxing Bao, Prasanna Parvathaneni, Bennett A. Landman

Herein, the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform the surface parcellation upon the inner, central and outer surfaces that are reconstructed from MaCRUISE.

Brain Segmentation

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