AIMC Topic: Image Processing, Computer-Assisted

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Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation.

Sensors (Basel, Switzerland)
With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of pl...

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.

Sensors (Basel, Switzerland)
One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate...

Micro-CT image denoising with an asymmetric perceptual convolutional network.

Physics in medicine and biology
Micro-CT has important applications in biomedical research due to its ability to perform high-precision 3D imaging of micro-architecture in a non-invasive way. Because of the limited power of the radiation source, it is difficult to obtain a high sig...

A Semiautomated Deep Learning Approach for Pancreas Segmentation.

Journal of healthcare engineering
Accurate pancreas segmentation from 3D CT volumes is important for pancreas diseases therapy. It is challenging to accurately delineate the pancreas due to the poor intensity contrast and intrinsic large variations in volume, shape, and location. In ...

Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment.

Computers in biology and medicine
Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing th...

Multi-Task Head Pose Estimation in-the-Wild.

IEEE transactions on pattern analysis and machine intelligence
We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top ...

Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization.

IEEE transactions on pattern analysis and machine intelligence
Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potential for improving representation and generalization abilities of deep C...

CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images.

Sensors (Basel, Switzerland)
Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and co...

Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.

Scientific reports
Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultim...

Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection.

Scientific reports
With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen da...