AIMC Topic: Image Processing, Computer-Assisted

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Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set.

Magnetic resonance in medicine
PURPOSE: To estimate pixel-wise predictive uncertainty for deep learning-based MR image reconstruction and to examine the impact of domain shifts and architecture robustness.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has uniq...

DEPICTER: Deep representation clustering for histology annotation.

Computers in biology and medicine
Automatic segmentation of histopathology whole-slide images (WSI) usually involves supervised training of deep learning models with pixel-level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully s...

Inverse radon transform with deep learning: an application in cardiac motion correction.

Physics in medicine and biology
. This paper addresses performing inverse radon transform (IRT) with artificial neural network (ANN) or deep learning, simultaneously with cardiac motion correction (MC). The suggested application domain is cardiac image reconstruction in emission or...

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks.

PloS one
For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific fe...

A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.

Journal of applied clinical medical physics
PURPOSE: Non-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties...

Generation of tissues outside the field of view (FOV) of radiation therapy simulation imaging based on machine learning and patient body outline (PBO).

Radiation oncology (London, England)
BACKGROUND: It is not unusual to see some parts of tissues are excluded in the field of view of CT simulation images. A typical mitigation is to avoid beams entering the missing body parts at the cost of sub-optimal planning.

A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images.

Scientific reports
Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body ...

Semi-supervised learning methods for weed detection in turf.

Pest management science
BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop ...

Understanding skin color bias in deep learning-based skin lesion segmentation.

Computer methods and programs in biomedicine
BACKGROUND: The field of dermatological image analysis using deep neural networks includes the semantic segmentation of skin lesions, pivotal for lesion analysis, pathology inference, and diagnoses. While biases in neural network-based dermatoscopic ...