DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation.

Sputum smear tests are critical for the diagnosis of respiratory diseases. Automatic segmentation of bacteria from sputum smear images is important for improving diagnostic efficiency. However, this remains a challenging task owing to the high interclass similarity among different categories of bacteria and the low contrast of the bacterial edges.

Multimodal cross enhanced fusion network for diagnosis of Alzheimer's disease and subjective memory complaints.

Deep learning methods using multimodal imagings have been proposed for the diagnosis of Alzheimer's disease (AD) and its early stages (SMC, subjective memory complaints), which may help to slow the progression of the disease through early intervention. However, current fusion methods for multimodal imagings are generally coarse and may lead to suboptimal results through the use of shared extractors or simple downscaling stitching. Another issue with diagnosing brain diseases is that they often affect multiple areas of the brain, making it important to consider potential connections throughout the brain.

BMNet: A New Region-Based Metric Learning Method for Early Alzheimer's Disease Identification With FDG-PET Images.

18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, existing FDG-PET-based researches are still insufficient for the identification of early MCI (EMCI) and late MCI (LMCI).

[Glasses-free Three Dimensional Endoscopic Display System with Multi-face Detection].

The glasses-free three dimensional(3D) endoscopic display system provides the surgeon with the depth information of the minimally invasive surgery scene obtained from the binocular perspective, which can effectively relieve the surgeon's posture fatigue and visual fatigue during the long-term surgery, and assist in the operation of surgical instruments more accurately to reduce the damage to the surrounding tissues of the operation area. However, the glasses-free 3D display device currently has the problem of a narrow optimal viewing zone and easy crosstalk, especially in the surgical teaching application scenario, which performs poorly. In order to overcome the limitation of the narrower field of view, we introduce deep learning algorithms to detect and locate multiple faces, fine-tune the 3D display grating of the endoscope, rearrange pixels, and change the best view area, so that more people can get the best view.