Diffusion models in medical imaging: A comprehensive survey
Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
The importance of resource awareness in artificial intelligence for healthcare
Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide
range of healthcare applications, from medical image computing and analysis to continuous …
range of healthcare applications, from medical image computing and analysis to continuous …
Ambiguous medical image segmentation using diffusion models
A Rahman, JMJ Valanarasu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Collective insights from a group of experts have always proven to outperform an individual's
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …
Carbontracker: Tracking and predicting the carbon footprint of training deep learning models
Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this
often comes at the cost of training models for extensive periods on specialized hardware …
often comes at the cost of training models for extensive periods on specialized hardware …
Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans
This paper performs a comprehensive study on the deep-learning-based computer-aided
diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by …
diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by …
Computer-aided classification of lung nodules on computed tomography images via deep learning technique
Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are
present. The management of small lung nodules noted on computed tomography scan is …
present. The management of small lung nodules noted on computed tomography scan is …
Lung cancer diagnosis and staging using firefly algorithm fuzzy C-means segmentation and support vector machine classification of lung nodules
Lung nodule segmentation is an important division of automated disease screening systems
in cancer identification. The morphological variations of lung nodules correspond to chances …
in cancer identification. The morphological variations of lung nodules correspond to chances …
Lung nodule classification using deep features in CT images
Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate,
which accounts for more than 17% percent of the total cancer related deaths. A large …
which accounts for more than 17% percent of the total cancer related deaths. A large …
The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans
SG Armato III, G McLennan, L Bidaut… - Medical …, 2011 - Wiley Online Library
Purpose: The development of computer‐aided diagnostic (CAD) methods for lung nodule
detection, classification, and quantitative assessment can be facilitated through a well …
detection, classification, and quantitative assessment can be facilitated through a well …
Comparison and evaluation of methods for liver segmentation from CT datasets
This paper presents a comparison study between 10 automatic and six interactive methods
for liver segmentation from contrast-enhanced CT images. It is based on results from the …
for liver segmentation from contrast-enhanced CT images. It is based on results from the …