A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.

Purpose: Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast lesions.

Automatic fetal biometry prediction using a novel deep convolutional network architecture.

Purpose: Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultrasound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network.

Methods: The proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates.

Accuracy of diffusion-weighted imaging in discriminating atypical vertebral haemangiomas from malignant masses in patients with vertebral lesions: a cross-sectional study.

Purpose: Vertebral haemangiomas are incidental findings in imaging modalities. Atypical haemangiomas are haeman-giomas rich in vascular tissue, and they are found to be hypointense in T1 sequences and hyperintense in T2 sequences, mimicking the findings of metastatic lesions. In the present study we aim to evaluate the ability of diffusion- weighted imaging to differentiate these two groups of vertebral lesions.