Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
Computer-aided diagnosis of skin cancer: a review
Cancer is currently one of the main health issues in the world. Among different varieties of
cancers, skin cancer is the most common cancer in the world and accounts for 75% of the …
cancers, skin cancer is the most common cancer in the world and accounts for 75% of the …
Medical image classification using a light-weighted hybrid neural network based on PCANet and DenseNet
Z Huang, X Zhu, M Ding, X Zhang - Ieee Access, 2020 - ieeexplore.ieee.org
Medical image classification plays an important role in disease diagnosis since it can
provide important reference information for doctors. The supervised convolutional neural …
provide important reference information for doctors. The supervised convolutional neural …
A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images
T Banzato, M Bernardini, GB Cherubini, A Zotti - BMC veterinary research, 2018 - Springer
Background Distinguishing between meningeal-based and intra-axial lesions by means of
magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas …
magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas …
A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images
Abstract Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging,
could support radiologists in classifying different kinds of breast lesions and then improve …
could support radiologists in classifying different kinds of breast lesions and then improve …
Reduction 93.7% time and power consumption using a memristor-based imprecise gradient update algorithm
The conventional computing system with the architecture of von Neumann has greatly
benefited our humans for past decades, while it is also suffered from low efficiency due to …
benefited our humans for past decades, while it is also suffered from low efficiency due to …
Bi-rads classification of breast cancer: a new pre-processing pipeline for deep models training
One of the main difficulties in the use of deep learning strategies in medical contexts is the
training set size. While these methods need large annotated training sets, these datasets are …
training set size. While these methods need large annotated training sets, these datasets are …
Multiple kernel-learning approach for medical image analysis
Technological advancements in medical imaging are generating petabytes of digital data.
From simple radiography to positron-emitted tomography (PET), diagnostic medical imaging …
From simple radiography to positron-emitted tomography (PET), diagnostic medical imaging …
Computer vision in esophageal cancer: a literature review
Esophageal cancer is a disease with a high prevalence that can be evaluated by a variety of
imaging modalities, including endoscopy, computed tomography, and positron emission …
imaging modalities, including endoscopy, computed tomography, and positron emission …
An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images
Abstract Background Positron Emission Tomography–Computed Tomography (PET/CT)
imaging is the basis for the evaluation of response-to-treatment of several oncological …
imaging is the basis for the evaluation of response-to-treatment of several oncological …