Search Results for author: Liansheng Wang

Found 36 papers, 23 papers with code

Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation

1 code implementation18 Mar 2024 Qian Dai, Dong Wei, Hong Liu, Jinghan Sun, Liansheng Wang, Yefeng Zheng

In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants' data.

Brain Tumor Segmentation Federated Learning +1

HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction

1 code implementation8 Mar 2024 Zhengrui Guo, Jiabo Ma, Yingxue Xu, Yihui Wang, Liansheng Wang, Hao Chen

Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patient care.

Medical Report Generation Multiple Instance Learning +3

Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction

1 code implementation29 Feb 2024 Hao Li, Ying Chen, Yifei Chen, Wenxian Yang, Bowen Ding, Yuchen Han, Liansheng Wang, Rongshan Yu

It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics.

Image Classification Language Modelling +3

Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models

1 code implementation22 Jan 2024 Chenyu Lian, Hong-Yu Zhou, Yizhou Yu, Liansheng Wang

Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained large language models has recently emerged as an effective approach to perform transfer learning on computer vision tasks.

Transfer Learning

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.


Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos

1 code implementation3 Oct 2023 Junhao Lin, Qian Dai, Lei Zhu, Huazhu Fu, Qiong Wang, Weibin Li, Wenhao Rao, Xiaoyang Huang, Liansheng Wang

We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos.

Lesion Segmentation Segmentation +1

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

Why is the winner the best?

no code implementations CVPR 2023 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.

Benchmarking Multi-Task Learning

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

Advancing Radiograph Representation Learning with Masked Record Modeling

1 code implementation30 Jan 2023 Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, Yizhou Yu

Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed.

Representation Learning

GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis

no code implementations11 Jan 2023 Hong-Yu Zhou, Chixiang Lu, Liansheng Wang, Yizhou Yu

Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks.

Contrastive Learning Lesion Segmentation +3

Lesion Guided Explainable Few Weak-shot Medical Report Generation

1 code implementation16 Nov 2022 Jinghan Sun, Dong Wei, Liansheng Wang, Yefeng Zheng

To this end, we propose a lesion guided explainable few weak-shot medical report generation framework that learns correlation between seen and novel classes through visual and semantic feature alignment, aiming to generate medical reports for diseases not observed in training.

Medical Report Generation

UNet-2022: Exploring Dynamics in Non-isomorphic Architecture

no code implementations27 Oct 2022 Jiansen Guo, Hong-Yu Zhou, Liansheng Wang, Yizhou Yu

These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.

Image Segmentation Lesion Segmentation +4

Personalizing Federated Medical Image Segmentation via Local Calibration

1 code implementation11 Jul 2022 Jiacheng Wang, Yueming Jin, Liansheng Wang

Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site.

Federated Learning Image Segmentation +3

A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos

2 code implementations1 Jul 2022 Zhi Lin, Junhao Lin, Lei Zhu, Huazhu Fu, Jing Qin, Liansheng Wang

Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos.

Lesion Classification Lesion Detection

XBound-Former: Toward Cross-scale Boundary Modeling in Transformers

1 code implementation2 Jun 2022 Jiacheng Wang, Fei Chen, Yuxi Ma, Liansheng Wang, Zhaodong Fei, Jianwei Shuai, Xiangdong Tang, Qichao Zhou, Jing Qin

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i. e., considerable size, shape and color variation, and ambiguous boundaries.

Lesion Segmentation Segmentation +1

Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation

no code implementations23 Apr 2022 Cong Xie, Hualuo Liu, Shilei Cao, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class.

Semantic Segmentation

Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network

no code implementations7 Mar 2022 Shuxin Wang, Shilei Cao, Zhizhong Chai, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset.

Segmentation Tumor Segmentation

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.


Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network

1 code implementation8 Nov 2021 Jiacheng Wang, Yueming Jin, Shuntian Cai, Hongzhi Xu, Pheng-Ann Heng, Jing Qin, Liansheng Wang

Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.

Multi-Task Learning Relation +1

Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification

1 code implementation9 Oct 2021 Jinghan Sun, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantages of both unsupervised and (pseudo-) supervised learning on the base dataset.

Classification Few-Shot Learning +2

Boundary-aware Transformers for Skin Lesion Segmentation

1 code implementation8 Oct 2021 Jiacheng Wang, Lan Wei, Liansheng Wang, Qichao Zhou, Lei Zhu, Jing Qin

Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer.

Inductive Bias Lesion Segmentation +2

Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video

1 code implementation28 Sep 2021 Jiacheng Wang, Yueming Jin, Liansheng Wang, Shuntian Cai, Pheng-Ann Heng, Jing Qin

On the other hand, we develop an active global memory to gather the global semantic correlation in long temporal range to current one, in which we gather the most informative frames derived from model uncertainty and frame similarity.

Optical Flow Estimation Segmentation

nnFormer: Interleaved Transformer for Volumetric Segmentation

2 code implementations7 Sep 2021 Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.

Image Segmentation Inductive Bias +3

RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation

no code implementations19 Jul 2021 Cong Xie, Shilei Cao, Dong Wei, HongYu Zhou, Kai Ma, Xianli Zhang, Buyue Qian, Liansheng Wang, Yefeng Zheng

Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance.

Computed Tomography (CT) Lesion Detection +1

Superpixel-Guided Label Softening for Medical Image Segmentation

no code implementations17 Jul 2020 Hang Li, Dong Wei, Shilei Cao, Kai Ma, Liansheng Wang, Yefeng Zheng

If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area.

Image Segmentation Medical Image Segmentation +2

ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images

no code implementations30 Jul 2017 Huangjing Lin, Hao Chen, Qi Dou, Liansheng Wang, Jing Qin, Pheng-Ann Heng

Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists.

Breast Cancer Detection whole slide images

Volume Calculation of CT lung Lesions based on Halton Low-discrepancy Sequences

no code implementations6 Jun 2017 Liansheng Wang, Shusheng Li, Shuo Li

With the uniform distribution of random points, our proposed method achieves more accurate results compared with other methods, which demonstrates the robustness and accuracy for the volume calculation of CT lung lesions.

Computed Tomography (CT)

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