Attention-guided erasing for enhanced transfer learning in breast abnormality classification.

Purpose: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography.

Methods: AGE utilizes attention head visualizations from DINO self-supervised pretraining to weakly localize regions of interest (ROI) in images.

Corrigendum to "Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery".

[This corrects the article DOI: 10.1155/2017/6028645.].

Registration of vascular structures using a hybrid mixture model.

Purpose: Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes.

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery.

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem.