Search Results for author: Danny Z. Chen

Found 46 papers, 16 papers with code

U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

1 code implementation29 Nov 2023 Yaopeng Peng, Milan Sonka, Danny Z. Chen

We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over state-of-the-art methods, while preserving memory and computational efficiency.

PHG-Net: Persistent Homology Guided Medical Image Classification

1 code implementation28 Nov 2023 Yaopeng Peng, Hongxiao Wang, Milan Sonka, Danny Z. Chen

The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion.

OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation

no code implementations24 Sep 2023 Yixuan Wu, Bo Zheng, Jintai Chen, Danny Z. Chen, Jian Wu

As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images.

Image Segmentation Medical Image Segmentation +5

GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels

no code implementations16 Sep 2023 Yixuan Wu, Jintai Chen, Jiahuan Yan, Yiheng Zhu, Danny Z. Chen, Jian Wu

Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden.

Contrastive Learning Image Segmentation +3

RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

no code implementations9 Sep 2023 Yizhe Zhang, Shuo Wang, Yejia Zhang, Danny Z. Chen

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e. g., 99. 5\% of the time).

Conformal Prediction Decision Making +3

SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

1 code implementation26 Aug 2023 Yizhe Zhang, Tao Zhou, Shuo Wang, Ye Wu, Pengfei Gu, Danny Z. Chen

Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.

Image Segmentation Lesion Segmentation +3

SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings

no code implementations23 Jul 2023 Yejia Zhang, Pengfei Gu, Nishchal Sapkota, Danny Z. Chen

Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions.

3D Shape Reconstruction Image Segmentation +4

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis

no code implementations17 Feb 2023 Yizhe Zhang, Danny Z. Chen

In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image analysis (MIA).

Image Classification Language Modelling

T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction

1 code implementation30 Nov 2022 Jiahuan Yan, Jintai Chen, Yixuan Wu, Danny Z. Chen, Jian Wu

Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction.

A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning

no code implementations15 Nov 2022 Yejia Zhang, Pengfei Gu, Nishchal Sapkota, Hao Zheng, Peixian Liang, Danny Z. Chen

High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance.

Image Segmentation Medical Image Segmentation +3

Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation

no code implementations15 Nov 2022 Yejia Zhang, Xinrong Hu, Nishchal Sapkota, Yiyu Shi, Danny Z. Chen

Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations.

Clustering Contrastive Learning +4

ConvFormer: Combining CNN and Transformer for Medical Image Segmentation

no code implementations15 Nov 2022 Pengfei Gu, Yejia Zhang, Chaoli Wang, Danny Z. Chen

(2) A residual-shaped hybrid stem based on a combination of convolutions and Enhanced DeTrans is developed to capture both local and global representations to enhance representation ability.

Image Segmentation Medical Image Segmentation +2

Robust Training of Graph Neural Networks via Noise Governance

1 code implementation12 Nov 2022 Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z. Chen, Jian Wu

To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.


Data-Driven Deep Supervision for Skin Lesion Classification

no code implementations4 Sep 2022 Suraj Mishra, Yizhe Zhang, Li Zhang, Tianyu Zhang, X. Sharon Hu, Danny Z. Chen

Specifically, we analyze the convolutional network's behavior (field-of-view) to find the location of deep supervision for improved feature extraction.

Classification Lesion Classification +1

Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models

1 code implementation1 Jul 2022 Yizhe Zhang, Suraj Mishra, Peixian Liang, Hao Zheng, Danny Z. Chen

We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted.

Image Segmentation Medical Image Segmentation +1

Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network

1 code implementation16 Jun 2022 Yuexin Bian, Jintai Chen, Xiaojun Chen, Xiaoxian Yang, Danny Z. Chen, Jian Wu

Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases.

Clinical Knowledge ECG Classification

H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation

no code implementations2 Jun 2022 Peixian Liang, Yizhe Zhang, Yifan Ding, Jianxu Chen, Chinedu S. Madukoma, Tim Weninger, Joshua D. Shrout, Danny Z. Chen

We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.

Image Segmentation Instance Segmentation +2

D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation

1 code implementation3 Jan 2022 Yixuan Wu, Kuanlun Liao, Jintai Chen, Jinhong Wang, Danny Z. Chen, Honghao Gao, Jian Wu

In this paper, we propose a new method called Dilated Transformer, which conducts self-attention for pair-wise patch relations captured alternately in local and global scopes.

Image Segmentation Medical Image Segmentation +2

DANets: Deep Abstract Networks for Tabular Data Classification and Regression

1 code implementation6 Dec 2021 Jintai Chen, Kuanlun Liao, Yao Wan, Danny Z. Chen, Jian Wu

A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks.


Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation

no code implementations10 Jul 2021 Hao Zheng, Jun Han, Hongxiao Wang, Lin Yang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen

Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks.

Image Segmentation Medical Image Segmentation +4

Image Complexity Guided Network Compression for Biomedical Image Segmentation

no code implementations6 Jul 2021 Suraj Mishra, Danny Z. Chen, X. Sharon Hu

Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network.

Image Segmentation Segmentation +1

Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision

1 code implementation12 May 2021 Jintai Chen, Xiangshang Zheng, Hongyun Yu, Danny Z. Chen, Jian Wu

For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints.

Self-Supervised Learning

Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation

no code implementations17 Apr 2021 Suraj Mishra, Danny Z. Chen, X. Sharon Hu

In this paper, we study retinal vessel segmentation by incorporating tiny vessel segmentation into our framework for the overall accurate vessel segmentation.

Retinal Vessel Segmentation Segmentation

Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods

no code implementations10 Feb 2021 Jintai Chen, Bohan Yu, Biwen Lei, Ruiwei Feng, Danny Z. Chen, Jian Wu

The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e. g., the Tanner-Whitehouse method) for bone age assessment.


Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels

no code implementations9 Feb 2021 Jintai Chen, Hongyun Yu, Ruiwei Feng, Danny Z. Chen, Jian Wu

In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities.

Image Classification Medical Image Classification

Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation

no code implementations17 Dec 2020 Hongxiao Wang, Hao Zheng, Jianxu Chen, Lin Yang, Yizhe Zhang, Danny Z. Chen

Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.

Clustering Image Generation +3

Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images

no code implementations7 Jun 2019 Yizhe Zhang, Michael T. C. Ying, Danny Z. Chen

Ablation study confirms the effectiveness of our proposed learning scheme for medical images.


SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation

no code implementations28 Feb 2019 Yizhe Zhang, Lin Yang, Hao Zheng, Peixian Liang, Colleen Mangold, Raquel G. Loreto, David. P. Hughes, Danny Z. Chen

To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images.

Data Augmentation Image Segmentation +1

Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation

no code implementations15 Jan 2019 Peixian Liang, Jianxu Chen, Hao Zheng, Lin Yang, Yizhe Zhang, Danny Z. Chen

The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders.

Image Segmentation Segmentation +1

CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation

1 code implementation6 Jan 2019 Suraj Mishra, Peixian Liang, Adam Czajka, Danny Z. Chen, X. Sharon Hu

Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations.

Image Segmentation Segmentation +1

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

1 code implementation10 Dec 2018 Hao Zheng, Yizhe Zhang, Lin Yang, Peixian Liang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen

In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.

3D Medical Imaging Segmentation Ensemble Learning +3

Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation

no code implementations28 Jun 2018 Zhuo Zhao, Lin Yang, Hao Zheng, Ian H. Guldner, Si-Yuan Zhang, Danny Z. Chen

Our approach needs only 3D bounding boxes for all instances and full voxel annotation for a small fraction of the instances, and uses a novel two-stage 3D instance segmentation model utilizing these two kinds of annotation, respectively.

3D Instance Segmentation Segmentation +1

A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs

no code implementations1 Feb 2018 Peixian Liang, Jianxu Chen, Pavel A. Brodskiy, Qinfeng Wu, Yejia Zhang, Yizhe Zhang, Lin Yang, Jeremiah J. Zartman, Danny Z. Chen

A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences.

Neuron Segmentation Using Deep Complete Bipartite Networks

no code implementations31 May 2017 Jianxu Chen, Sreya Banerjee, Abhinav Grama, Walter J. Scheirer, Danny Z. Chen

We propose a new FCN-type deep learning model, called deep complete bipartite networks (CB-Net), and a new scheme for leveraging approximate instance-wise annotation to train our pixel-wise prediction model.


Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

no code implementations29 May 2017 Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

The high computation throughput and memory bandwidth of graphics processing units (GPUs) make GPUs a natural choice for accelerating convolution operations.

Matroid and Knapsack Center Problems

1 code implementation4 Jan 2013 Danny Z. Chen, Jian Li, Hongyu Liang, Haitao Wang

We also consider the outlier version of the problem where a given number of vertices can be excluded as the outliers from the solution.

Data Structures and Algorithms Discrete Mathematics

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