Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs

Artificial intelligence assisted display in thoracic surgery: development and possibilities

“…It provides powerful technical support for precise segmentectomy by helping locate the lesions, identify targeted blood vessels and bronchus, surgical margins, and reveal anatomical variations (10) (Figure 1D-1F). Prior studies reported that the accurate reconstruction rate of the PA could reach 95.2% (35,36), and the segmentation similarity of current AI models can reach a dice score above 0.80 (37).…”

Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods

“…The discriminator network penalizes, among standard terms, also the overlap of lung masks and additionally a cycle consistency loss is used. Small differences are also the subject of [11] and [14], which also start with affine and B-spline registration within segmented lung regions on a private data set, excluding cases with too large deformations. They penalize first and second derivatives of the dense displacement field during CNN training in order to obtain better local matching in an unsupervised setting.…”

Artificial Intelligence Based Deep Bayesian Neural Network (DBNN) Toward Personalized Treatment of Leukemia with Stem Cells

“…We deploy pooling which is a downsampling technique to capture the spatial invariance properties of the data. Suppose, the shape or position of a leukemia on different images may differ and the network can get confused or miss some key information about that tumor in such situations [37][38][39][40][41][42][43][44][45][46][47]. Pooling operation tries to assure that the NN does not miss any important information about the data.…”

“…The management of adult leukemia patients has been largely guided by baseline clinical parameters including age at diagnosis, white blood cell (WBC) count, whether disease presentation is de novo, treatment-related, or secondary to prior disease. Additionally, an ever-expanding set of specific karyotype and gene mutations are being incorporated into patient risk stratification schemes, producing broadly favorable, intermediate, and adverse patient risk groups as described in [7,8]. This standard treatment is clearly not curative for the vast majority of leukemia patients as evidenced by the overall survival rate of < 50% according to [9].…”