AIMC Topic: Adenocarcinoma

Clear Filters Showing 41 to 50 of 243 articles

Layer-selective deep representation to improve esophageal cancer classification.

Medical & biological engineering & computing
Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliabili...

Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Histopathological assessment of esophageal biopsies is a key part in the management of patients with Barrett esophagus (BE) but prone to observer variability and reliable diagnostic methods are needed. Artificial intelligence (AI) is emerging as a po...

Precise and automated lung cancer cell classification using deep neural network with multiscale features and model distillation.

Scientific reports
Lung diseases globally impose a significant pathological burden and mortality rate, particularly the differential diagnosis between adenocarcinoma, squamous cell carcinoma, and small cell lung carcinoma, which is paramount in determining optimal trea...

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

Journal of medical radiation sciences
INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accur...

Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles.

Computers in biology and medicine
Distant metastasis of cancer is a significant contributor to cancer-related complications, and early identification of unidentified stomach adenocarcinoma is crucial for a positive prognosis. Changes inDNA methylation are being increasingly recognize...

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given sa...

Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort.

Pathology
Prostate and breast cancer incidence rates have been on the rise in Japan, emphasising the need for precise histopathological diagnosis to determine patient prognosis and guide treatment decisions. However, existing diagnostic methods face numerous c...