CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation

no code implementations • 15 Jul 2022 • Jianwei Lin, Jiatai Lin, Cheng Lu, Hao Chen, Huan Lin, Bingchao Zhao, Zhenwei Shi, Bingjiang Qiu, Xipeng Pan, Zeyan Xu, Biao Huang, Changhong Liang, Guoqiang Han, Zaiyi Liu, Chu Han

To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder.

Brain Tumor Segmentation Clinical Knowledge +3

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

no code implementations • 17 May 2022 • Yuhao Mo, Chu Han, Yu Liu, Min Liu, Zhenwei Shi, Jiatai Lin, Bingchao Zhao, Chunwang Huang, Bingjiang Qiu, Yanfen Cui, Lei Wu, Xipeng Pan, Zeyan Xu, Xiaomei Huang, Zaiyi Liu, Ying Wang, Changhong Liang

In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations.

Anatomy Classification +1

Recurrent convolutional neural networks for mandible segmentation from computed tomography

no code implementations • 13 Mar 2020 • Bingjiang Qiu, Jiapan Guo, Joep Kraeima, Haye H. Glas, Ronald J. H. Borra, Max J. H. Witjes, Peter M. A. van Ooijen

The recurrent structure guides the system to exploit relevant and important information from adjacent slices, while the SegCNN component focuses on the mandible shapes from a single CT slice.

Segmentation

3D segmentation of mandible from multisectional CT scans by convolutional neural networks

no code implementations • 18 Sep 2018 • Bingjiang Qiu, Jiapan Guo, J. Kraeima, R. J. H. Borra, M. J. H. Witjes, P. M. A. van Ooijen

The proposed convolutional neural network adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three different planes into a 3D segmentation.

Segmentation