Search Results for author: Lisheng Wang

Found 14 papers, 6 papers with code

Inferior Alveolar Nerve Segmentation in CBCT images using Connectivity-Based Selective Re-training

1 code implementation18 Aug 2023 Yusheng Liu, Rui Xin, Tao Yang, Lisheng Wang

Inferior Alveolar Nerve (IAN) canal detection in CBCT is an important step in many dental and maxillofacial surgery applications to prevent irreversible damage to the nerve during the procedure. The ToothFairy2023 Challenge aims to establish a 3D maxillofacial dataset consisting of all sparse labels and partial dense labels, and improve the ability of automatic IAN segmentation.

Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation

no code implementations14 Mar 2023 Tao Yang, Lisheng Wang

If ceT1 scans and their annotations can be used for unsupervised learning of hrT2 scans, the performance of Koos classifi-cation using unlabeled hrT2 scans will be greatly improved.

Classification Domain Adaptation

3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations

no code implementations7 Feb 2023 Huai Chen, Xiuying Wang, Lisheng Wang

Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images; secondly, based on the similarity of the tree-shaped morphology between 2D and 3D vessels, the Crop-and-Overlap strategy is presented to generate reference masks from 2D structure-agnostic vessel annotations, which are fit for varied vascular structures, and the adversarial loss is introduced to guide the tree-shaped morphology of 3D vessels; thirdly, the temporal consistency loss is proposed to foster the training stability and keep the model updated smoothly.

A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment

no code implementations19 Jul 2022 Yusheng Liu, Zhongchen Zhao, Lisheng Wang

Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance.

Unsupervised Local Discrimination for Medical Images

1 code implementation21 Aug 2021 Huai Chen, Renzhen Wang, Xiuying Wang, Jieyu Li, Qu Fang, Hui Li, Jianhao Bai, Qing Peng, Deyu Meng, Lisheng Wang

To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.

Contrastive Learning Lesion Segmentation +1

Unsupervised Learning of Local Discriminative Representation for Medical Images

1 code implementation17 Dec 2020 Huai Chen, Jieyu Li, Renzhen Wang, YiJie Huang, Fanrui Meng, Deyu Meng, Qing Peng, Lisheng Wang

However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks.

Clustering Representation Learning

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19

no code implementations10 Dec 2020 Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang

In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19.

COVID-19 Diagnosis Transfer Learning

MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

no code implementations25 Dec 2018 Huai Chen, Yuxiao Qi, Yong Yin, Tengxiang Li, Xiaoqing Liu, Xiuli Li, Guanzhong Gong, Lisheng Wang

Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC.

3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

2 code implementations27 Jun 2018 Yi-Jie Huang, Qi Dou, Zi-Xian Wang, Li-Zhi Liu, Ying Jin, Chao-Feng Li, Lisheng Wang, Hao Chen, Rui-Hua Xu

With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field.

Image Segmentation Multi-Task Learning +1

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