Qianjin Feng

TL;DR: The augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types.

TL;DR: For low-dose CT image restoration, the presented ndiNLM method is robust in preserving the spatial resolution and identifying the low-contrast structure and may be useful for some clinical applications such as in perfusion imaging, radiotherapy, tumor surveillance, etc.

TL;DR: This work proposes a novel automatic tumor segmentation method for MRI images that treats tumor segmentsation as a classification problem and considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance.

TL;DR: This paper proposes a novel feature extraction framework for retrieving brain tumors in T1-weighted contrast-enhanced MRI images and demonstrates the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.

TL;DR: Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.