Search Results for author: Cheng Ouyang

Found 26 papers, 16 papers with code

Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis

no code implementations15 Sep 2024 Liu Li, Hanchun Wang, Matthew Baugh, Qiang Ma, Weitong Zhang, Cheng Ouyang, Daniel Rueckert, Bernhard Kainz

Directly training a post-processing model to mitigate topological errors often fails as such models tend to be biased towards the topological errors of a target segmentation network.

Image Segmentation Medical Image Segmentation +1

Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics

no code implementations19 Jun 2024 Weitong Zhang, Chengqi Zang, Liu Li, Sarah Cechnicka, Cheng Ouyang, Bernhard Kainz

We uncover several strategies that inherently enhance the stability and generalizability of diffusion models for inverse problems and introduce a novel score-based diffusion framework, the \textbf{D}ynamics-aware S\textbf{D}E \textbf{D}iffusion \textbf{G}enerative \textbf{M}odel (D$^3$GM).

Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere

no code implementations26 May 2024 Hongwei Bran Li, Cheng Ouyang, Tamaz Amiranashvili, Matthew S. Rosen, Bjoern Menze, Juan Eugenio Iglesias

Self-supervised contrastive learning has predominantly adopted deterministic methods, which are not suited for environments characterized by uncertainty and noise.

Contrastive Learning Out-of-Distribution Detection

A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts

1 code implementation16 May 2024 Xinru Zhang, Ni Ou, Berke Doga Basaran, Marco Visentin, Mengyun Qiao, Renyang Gu, Cheng Ouyang, Yaou Liu, Paul M. Matthew, Chuyang Ye, Wenjia Bai

In this work, we propose a universal foundation model for 3D brain lesion segmentation, which can automatically segment different types of brain lesions for input data of various imaging modalities.

Lesion Segmentation Segmentation

Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement

1 code implementation11 Mar 2024 Che Liu, Zhongwei Wan, Cheng Ouyang, Anand Shah, Wenjia Bai, Rossella Arcucci

Through multimodal learning on ECG records and associated reports, MERL is capable of performing zero-shot ECG classification with text prompts, eliminating the need for training data in downstream tasks.

Clinical Knowledge Descriptive +5

G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training

1 code implementation3 Dec 2023 Che Liu, Cheng Ouyang, Sibo Cheng, Anand Shah, Wenjia Bai, Rossella Arcucci

G2D achieves superior performance across 6 medical imaging tasks and 25 diseases, particularly in semantic segmentation, which necessitates fine-grained, semantically-grounded image features.

object-detection Object Detection +5

Context Label Learning: Improving Background Class Representations in Semantic Segmentation

1 code implementation16 Dec 2022 Zeju Li, Konstantinos Kamnitsas, Cheng Ouyang, Chen Chen, Ben Glocker

The results demonstrate that CoLab can guide the segmentation model to map the logits of background samples away from the decision boundary, resulting in significantly improved segmentation accuracy.

Segmentation Semantic Segmentation

Improved post-hoc probability calibration for out-of-domain MRI segmentation

1 code implementation4 Aug 2022 Cheng Ouyang, Shuo Wang, Chen Chen, Zeju Li, Wenjia Bai, Bernhard Kainz, Daniel Rueckert

In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable.

Image Segmentation MRI segmentation +2

MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation

1 code implementation2 Jun 2022 Chen Chen, Zeju Li, Cheng Ouyang, Matt Sinclair, Wenjia Bai, Daniel Rueckert

We propose a novel data augmentation framework called MaxStyle, which maximizes the effectiveness of style augmentation for model OOD performance.

Data Augmentation Decoder +3

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation

1 code implementation24 Nov 2021 Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, Daniel Rueckert

In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data is only available from one source domain, which is common in medical imaging applications.

Data Augmentation Image Segmentation +4

Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

2 code implementations2 Jul 2021 Chen Chen, Kerstin Hammernik, Cheng Ouyang, Chen Qin, Wenjia Bai, Daniel Rueckert

In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples.

Data Augmentation Image Reconstruction +4

Realistic Adversarial Data Augmentation for MR Image Segmentation

1 code implementation23 Jun 2020 Chen Chen, Chen Qin, Huaqi Qiu, Cheng Ouyang, Shuo Wang, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert

In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.

Data Augmentation Image Segmentation +3

Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation

no code implementations20 Aug 2019 Chen Chen, Cheng Ouyang, Giacomo Tarroni, Jo Schlemper, Huaqi Qiu, Wenjia Bai, Daniel Rueckert

In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire.

Image Segmentation Segmentation +3

VS-Net: Variable splitting network for accelerated parallel MRI reconstruction

1 code implementation19 Jul 2019 Jinming Duan, Jo Schlemper, Chen Qin, Cheng Ouyang, Wenjia Bai, Carlo Biffi, Ghalib Bello, Ben Statton, Declan P. O'Regan, Daniel Rueckert

In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data.

Deep Learning MRI Reconstruction +1

Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation

no code implementations5 Jul 2019 Cheng Ouyang, Konstantinos Kamnitsas, Carlo Biffi, Jinming Duan, Daniel Rueckert

Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data.

Image Segmentation Medical Image Segmentation +3

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

2 code implementations19 Dec 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng-Ann Heng

In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.

Cardiac Segmentation Domain Adaptation +2

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

2 code implementations29 Apr 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng

The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions.

Transfer Learning Unsupervised Domain Adaptation

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