AIMC Topic: Image Processing, Computer-Assisted

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Knowledge-based dose prediction models for head and neck cancer are strongly affected by interorgan dependency and dataset inconsistency.

Medical physics
PURPOSE: The goal of this study was to generate a large treatment plan database for head and neck (H&N) cancer patients that can be considered as the gold standard to train and validate models for knowledge-based (KB) treatment planning and QA. With ...

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Journal of magnetic resonance imaging : JMRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety ...

Analysis of intensity normalization for optimal segmentation performance of a fully convolutional neural network.

Zeitschrift fur medizinische Physik
INTRODUCTION: Convolutional neural networks have begun to surpass classical statistical- and atlas based machine learning techniques in medical image segmentation in recent years, proving to be superior in performance and speed. However, a major chal...

MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.

Medical image analysis
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectiv...

'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification.

eLife
Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but the relationship between artificial and neural representations of complex visual form has not been el...

PET Image Reconstruction Using Deep Image Prior.

IEEE transactions on medical imaging
Recently, deep neural networks have been widely and successfully applied in computer vision tasks and have attracted growing interest in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need for large...

Deep Learning in Image Cytometry: A Review.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is ap...

Dual-mode artificially-intelligent diagnosis of breast tumours in shear-wave elastography and B-mode ultrasound using deep polynomial networks.

Medical engineering & physics
The main goal of this study is to build an artificial intelligence (AI) architecture for automated extraction of dual-modal image features from both shear-wave elastography (SWE) and B-mode ultrasound, and to evaluate the AI architecture for classifi...

The Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization.

IEEE transactions on pattern analysis and machine intelligence
Fine-grained classification describes the automated recognition of visually similar object categories like birds species. Previous works were usually based on explicit pose normalization, i.e., the detection and description of object parts. However, ...

Automated detection of erythema migrans and other confounding skin lesions via deep learning.

Computers in biology and medicine
Lyme disease can lead to neurological, cardiac, and rheumatologic complications when untreated. Timely recognition of the erythema migrans rash of acute Lyme disease by patients and clinicians is crucial to early diagnosis and treatment. Our objectiv...