AI applications in renal pathology
The explosive growth of artificial intelligence (AI) technologies, especially deep learning
methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare …
methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare …
Tutorial on the use of deep learning in diffuse optical tomography
Diffuse optical tomography using deep learning is an emerging technology that has found
impressive medical diagnostic applications. However, creating an optical imaging system …
impressive medical diagnostic applications. However, creating an optical imaging system …
[HTML][HTML] An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset
Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient
to meet the body's physiological needs. It affects billions of people worldwide. An early …
to meet the body's physiological needs. It affects billions of people worldwide. An early …
Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI
are providing promising results, leading towards a revolution in the radiologists' workflow …
are providing promising results, leading towards a revolution in the radiologists' workflow …
[HTML][HTML] Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies
Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney
allograft failure. Here we sought an objective, quantitative pathological assessment of these …
allograft failure. Here we sought an objective, quantitative pathological assessment of these …
Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN
The accurate segmentation and identification of vertebrae presents the foundations for spine
analysis including fractures, malfunctions and other visual insights. The large-scale …
analysis including fractures, malfunctions and other visual insights. The large-scale …
An automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography images
Spine image analysis is based on the accurate segmentation and vertebrae recognition of
the spine. Several deep learning models have been proposed for spine segmentation and …
the spine. Several deep learning models have been proposed for spine segmentation and …
Ndg-cam: Nuclei detection in histopathology images with semantic segmentation networks and grad-cam
Nuclei identification is a fundamental task in many areas of biomedical image analysis
related to computational pathology applications. Nowadays, deep learning is the primary …
related to computational pathology applications. Nowadays, deep learning is the primary …
Focal dice loss-based V-Net for liver segments classification
Liver segmentation is a crucial step in surgical planning from computed tomography scans.
The possibility to obtain a precise delineation of the liver boundaries with the exploitation of …
The possibility to obtain a precise delineation of the liver boundaries with the exploitation of …
Boundary-aware glomerulus segmentation: toward one-to-many stain generalization
The growing availability of scanned whole-slide images (WSIs) has allowed
nephropathology to open new possibilities for medical decision-making over high-resolution …
nephropathology to open new possibilities for medical decision-making over high-resolution …