Jingfeng Suo

TL;DR: A deep learning architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE) that integrates feature learning with feature selection on SWE is built and may be potentially used in clinical computer-aided diagnosis of breast cancer.

TL;DR: For a data set containing 42 malignant and 75 benign tumors from 117 patients, seven selected sonoelastomic features achieved an area under the receiver operating characteristic curve of 0.917, revealing superiority over the principal component analysis, deep polynomial networks and manually selected features.

TL;DR: Dual-modal features can be extracted from RTE and B-mode ultrasound with computer assistance and are valuable for discrimination between benign and metastatic lymph nodes, which could be potentially used in daily clinical practice for assessing axillary metastasis in breast cancer patients.

TL;DR: A random binary hashing ( RBH) based PCANet (RBH-PCANet), which can generate multiple randomly encoded binary codes to provide more information, is proposed, which achieves best performance compared with other unsupervised deep learning algorithms.

TL;DR: The radiomics features from dual-modal ultrasound (elastography and B-mode) have demonstrated good performance for classification and have potential to be applied to clinical diagnosis of axillary lymph node metastasis.