Cross-view discrepancy-dependency network for volumetric medical image segmentation.

The limited data poses a crucial challenge for deep learning-based volumetric medical image segmentation, and many methods have tried to represent the volume by its subvolumes (i.e., multi-view slices) for alleviating this issue.

Multi-level fusion network for mild cognitive impairment identification using multi-modal neuroimages.

. Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD) which is an irreversible progressive neurodegenerative disease and its early diagnosis and intervention are of great significance. Recently, many deep learning methods have demonstrated the advantages of multi-modal neuroimages in MCI identification task.

MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework.

Background And Objective: Predicting the malignant potential of breast lesions based on breast ultrasound (BUS) images is a crucial component of computer-aided diagnosis system for breast cancers. However, since breast lesions in BUS images generally have various shapes with relatively low contrast and present complex textures, it still remains challenging to accurately identify the malignant potential of breast lesions.

Methods: In this paper, we propose a multi-scale gradational-order fusion framework to make full advantages of multi-scale representations incorporating with gradational-order characteristics of BUS images for breast lesions classification.

Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image.

Deep learning methods, especially convolutional neural networks, have advanced the breast lesion classification task using breast ultrasound (BUS) images. However, constructing a highly-accurate classification model still remains challenging due to complex pattern, relatively-low contrast and fuzzy boundary existing between lesion regions (i.e.

SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image.

Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e.

Breast ultrasound image segmentation: A coarse-to-fine fusion convolutional neural network.

Purpose: Breast ultrasound (BUS) image segmentation plays a crucial role in computer-aided diagnosis systems for BUS examination, which are useful for improved accuracy of breast cancer diagnosis. However, such performance remains a challenging task owing to the poor image quality and large variations in the sizes, shapes, and locations of breast lesions. In this paper, we propose a new convolutional neural network with coarse-to-fine feature fusion to address the aforementioned challenges.