Search Results for author: Donghuan Lu

Found 23 papers, 11 papers with code

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations

1 code implementation4 Dec 2023 Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng

Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.


Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

1 code implementation22 Sep 2023 Dong Wei, Yawen Huang, Donghuan Lu, Yuexiang Li, Yefeng Zheng

Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers.


You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray

no code implementations18 Jul 2023 Jinghan Sun, Dong Wei, Zhe Xu, Donghuan Lu, Hong Liu, Liansheng Wang, Yefeng Zheng

Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively.

Anomaly Detection Pseudo Label

M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities

1 code implementation9 Mar 2023 Hong Liu, Dong Wei, Donghuan Lu, Jinghan Sun, Liansheng Wang, Yefeng Zheng

In the first stage, a multimodal masked autoencoder (M3AE) is proposed, where both random modalities (i. e., modality dropout) and random patches of the remaining modalities are masked for a reconstruction task, for self-supervised learning of robust multimodal representations against missing modalities.

Brain Tumor Segmentation Representation Learning +3

RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training

no code implementations1 Mar 2023 Lianyu Zhou, Dong Wei, Donghuan Lu, Wei Xue, Liansheng Wang, Yefeng Zheng

As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline in CT slices.

Lesion Segmentation Weakly supervised segmentation

Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

1 code implementation18 Jul 2022 Xinyu Shi, Dong Wei, Yu Zhang, Donghuan Lu, Munan Ning, Jiashun Chen, Kai Ma, Yefeng Zheng

A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images.

Few-Shot Semantic Segmentation Segmentation +1

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images

1 code implementation4 Mar 2022 Hong Liu, Dong Wei, Donghuan Lu, Yuexiang Li, Kai Ma, Liansheng Wang, Yefeng Zheng

To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs.


All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

1 code implementation28 Sep 2021 Zhe Xu, Yixin Wang, Donghuan Lu, Lequan Yu, Jiangpeng Yan, Jie Luo, Kai Ma, Yefeng Zheng, Raymond Kai-yu Tong

Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?

Brain Tumor Segmentation Image Segmentation +3

Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network

no code implementations12 Sep 2021 Da Ma, Donghuan Lu, Karteek Popuri, Mirza Faisal Beg

Frontotemporal dementia and Alzheimer's disease are two common forms of dementia and are easily misdiagnosed as each other due to their similar pattern of clinical symptoms.

Binary Classification Data Augmentation +1

Multi-Anchor Active Domain Adaptation for Semantic Segmentation

2 code implementations ICCV 2021 Munan Ning, Donghuan Lu, Dong Wei, Cheng Bian, Chenglang Yuan, Shuang Yu, Kai Ma, Yefeng Zheng

Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the real-world target-domain samples.

Active Learning Domain Adaptation +1

Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration

no code implementations6 Jul 2021 Zhe Xu, Jie Luo, Donghuan Lu, Jiangpeng Yan, Sarah Frisken, Jayender Jagadeesan, William Wells III, Xiu Li, Yefeng Zheng, Raymond Tong

Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness.

Image Registration

Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

1 code implementation3 Jun 2021 Zhe Xu, Donghuan Lu, Yixin Wang, Jie Luo, Jayender Jagadeesan, Kai Ma, Yefeng Zheng, Xiu Li

Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data.

A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation

no code implementations20 Jul 2020 Munan Ning, Cheng Bian, Donghuan Lu, Hong-Yu Zhou, Shuang Yu, Chenglang Yuan, Yang Guo, Yaohua Wang, Kai Ma, Yefeng Zheng

Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.

Deep Image Clustering with Category-Style Representation

1 code implementation ECCV 2020 Junjie Zhao, Donghuan Lu, Kai Ma, Yu Zhang, Yefeng Zheng

In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.

Clustering Deep Clustering +1

Quality Control of Neuron Reconstruction Based on Deep Learning

no code implementations19 Mar 2020 Donghuan Lu, Sujun Zhao, Peng Xie, Kai Ma, Li-Juan Liu, Yefeng Zheng

To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper.

Binary Classification

Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence

no code implementations7 Dec 2019 Donghuan Lu, Morgan Heisler, Da Ma, Setareh Dabiri, Sieun Lee, Gavin Weiguang Ding, Marinko V. Sarunic, Mirza Faisal Beg

Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye.

Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

no code implementations2 Nov 2017 Karteek Popuri, Rakesh Balachandar, Kathryn Alpert, Donghuan Lu, Mahadev Bhalla, Ian Mackenzie, Robin Ging-Yuek Hsiung, Lei Wang, Mirza Faisal Beg, the Alzhemier's Disease Neuroimaging Initiative

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model.

General Classification

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