AIMC Topic: Image Processing, Computer-Assisted

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Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
While still in its infancy, the application of deep convolutional neural networks in veterinary diagnostic imaging is a rapidly growing field. The preferred deep learning architecture to be employed is convolutional neural networks, as these provide ...

Development of a Lightweight Crop Disease Image Identification Model Based on Attentional Feature Fusion.

Sensors (Basel, Switzerland)
Crop diseases are one of the important factors affecting crop yield and quality and are also an important research target in the field of agriculture. In order to quickly and accurately identify crop diseases, help farmers to control crop diseases in...

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.

International journal of environmental research and public health
BACKGROUND: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. D...

Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length.

Medical image analysis
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the a...

Interpretability-Guided Inductive Bias For Deep Learning Based Medical Image.

Medical image analysis
Deep learning methods provide state of the art performance for supervised learning based medical image analysis. However it is essential that trained models extract clinically relevant features for downstream tasks as, otherwise, shortcut learning an...

Unsupervised domain adaptation method for segmenting cross-sectional CCA images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neur...

Adaptive Modular Convolutional Neural Network for Image Recognition.

Sensors (Basel, Switzerland)
Image recognition has long been one of the research hotspots in computer vision tasks. The development of deep learning is rapid in recent years, and convolutional neural networks usually need to be designed with fixed resources. If sufficient resour...

A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.

Radiation oncology (London, England)
BACKGROUND: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.

SAFARI: shape analysis for AI-segmented images.

BMC medical imaging
BACKGROUND: Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to transl...

We got nuts! use deep neural networks to classify images of common edible nuts.

Nutrition and health
BACKGROUND: Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns.