Medical Image Segmentation Task

Medical image segmentation aims to automatically delineate specific anatomical structures or pathologies within medical images, aiding diagnosis and treatment planning. Current research heavily focuses on improving accuracy and efficiency using various architectures, including U-Net variations, Transformers, and hybrid models that combine convolutional neural networks with attention mechanisms or state-space models, often incorporating techniques like test-time training and prompt engineering. These advancements are crucial for improving the speed and accuracy of medical image analysis, ultimately leading to better patient care and accelerating medical research.

Papers

July 11, 2022

Personalizing Federated Medical Image Segmentation via Local Calibration
Jiacheng Wang, Yueming Jin, Liansheng Wang
Medical Image Segmentation Task Personalized Model Consistent Prediction Federated Medical Image Segmentation Aware Calibration

June 21, 2022

Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency
Jie Yang, Ye Zhu, Chaoqun Wang, Zhen Li, Ruimao Zhang
Multi Modal Data Medical Image Segmentation Task Cross Modality Semantic Consistency Cardiac Segmentation Abdominal Multi Organ Segmentation

June 2, 2022

MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet
Nan Wang, Shaohui Lin, Xiaoxiao Li, Ke Li, Yunhang Shen, Yue Gao, Lizhuang Ma
Transformer Based Multi Scale Medical Image Segmentation Task 3D Medical Image Segmentation Transformer Based Network TransUnet Model Transformer Based UNet

June 1, 2022

Dynamic Linear Transformer for 3D Biomedical Image Segmentation
Zheyuan Zhang, Ulas Bagci
Transformer Based Medical Image Segmentation Task 3D Medical Image Segmentation Linear Transformer Novel Transformer Architecture

May 5, 2022

Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation
Yuxin Kang, Hansheng Li, Xuan Zhao, Dongqing Hu, Feihong Liu, Lei Cui, Jun Feng, Lin Yang
Medical Image Segmentation Domain Generalization Medical Image Segmentation Task Synergistic Representation Learning

March 18, 2022

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation
An Xu, Wenqi Li, Pengfei Guo, Dong Yang, Holger Roth, Ali Hatamizadeh, Can Zhao, Daguang Xu, Heng Huang, Ziyue Xu
Deep Learning Medical Image Segmentation Medical Image Segmentation Task Generalization Gap Federated Medical Image Segmentation

March 2, 2022

Contextual Attention Network: Transformer Meets U-Net
Reza Azad, Moein Heidari, Yuli Wu, Dorit Merhof
Transformer Based Medical Image Segmentation U Net Attention Network Medical Image Segmentation Task

February 7, 2022

SODA: Self-organizing data augmentation in deep neural networks -- Application to biomedical image segmentation tasks
Arnaud Deleruyelle, John Klein, Cristian Versari
Neural Network Deep Neural Network Data Augmentation Application Proficiency Natural Gradient Soft Augmentation Medical Image Segmentation Task Meta Algorithm

January 21, 2022

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation
Zhuowei Li, Zihao Liu, Zhiqiang Hu, Qing Xia, Ruiqin Xiong, Shaoting Zhang, Dimitris Metaxas, Tingting Jiang
Contrastive Learning Medical Image Segmentation Medical Image Segmentation Task Contrastive Pretraining Contrastive Regularization Selective Neural Network

December 28, 2021

SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma
Zexi Huang, Lihua Guo, Xin Yang, Sijuan Huang
Segmentation Performance Medical Image Segmentation Task Pyramid Network Human Organ Nasopharyngeal Carcinoma Risk Segmentation Organ at Risk

December 22, 2021

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
Agostina Larrazabal, Cesar Martinez, Jose Dolz, Enzo Ferrante
Medical Image Segmentation Segmentation Network Medical Image Segmentation Task Maximum Entropy Segmentation Quality Segmentation Loss Unreliable Prediction

Medical Image Segmentation Task

Papers

Personalizing Federated Medical Image Segmentation via Local Calibration

Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency

MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet

Dynamic Linear Transformer for 3D Biomedical Image Segmentation

Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

Contextual Attention Network: Transformer Meets U-Net

SODA: Self-organizing data augmentation in deep neural networks -- Application to biomedical image segmentation tasks

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation