An artificial intelligence algorithm for the classification of sphenoid sinus pneumatisation on sinus computed tomography scans.

Background: Classifying sphenoid pneumatisation is an important but often overlooked task in reporting sinus CT scans. Artificial intelligence (AI) and one of its key methods, convolutional neural networks (CNNs), can create algorithms that can learn from data without being programmed with explicit rules and have shown utility in radiological image classification.

Objective: To determine if a trained CNN can accurately classify sphenoid sinus pneumatisation on CT sinus imaging.

Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review.

Background: Predicting morphological changes to anatomical structures from 3D shapes such as blood vessels or appearance of the face is a growing interest to clinicians. Machine learning (ML) has had great success driving predictions in 2D, however, methods suitable for 3D shapes are unclear and the use cases unknown.

Objective And Methods: This systematic review aims to identify the clinical implementation of 3D shape prediction and ML workflows.

Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images.

The identification of melanoma involves an integrated analysis of skin lesion images acquired using clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic details from clinical images. Visual melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC), which involves identifying specific characteristics of skin lesions.

Convolutional Neural Networks in ENT Radiology: Systematic Review of the Literature.

Introduction: Convolutional neural networks (CNNs) represent a state-of-the-art methodological technique in AI and deep learning, and were specifically created for image classification and computer vision tasks. CNNs have been applied in radiology in a number of different disciplines, mostly outside otolaryngology, potentially due to a lack of familiarity with this technology within the otolaryngology community. CNNs have the potential to revolutionize clinical practice by reducing the time required to perform manual tasks.

Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta-analysis.

Objectives: To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy.

Design: Systematic review and meta-analysis.

Methods: Using the PRISMA guidelines, nine online databases were searched for articles that used AI computer vision algorithms developed from various methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees and k-nearest neighbours) to classify otoscopic images.

An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children.

Objective: To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children.

Study Design: Retrospective observational study.

Setting: Tertiary referral center.

Fused feature signatures to probe tumour radiogenomics relationships.

Radiogenomics relationships (RRs) aims to identify statistically significant correlations between medical image features and molecular characteristics from analysing tissue samples. Previous radiogenomics studies mainly relied on a single category of image feature extraction techniques (ETs); these are (i) handcrafted ETs that encompass visual imaging characteristics, curated from knowledge of human experts and, (ii) deep ETs that quantify abstract-level imaging characteristics from large data. Prior studies therefore failed to leverage the complementary information that are accessible from fusing the ETs.

Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning.

Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of soft-tissue sarcomas (STSs). Distant metastases (DM) are the leading cause of death in STS patients and early detection is important to effectively manage tumors with surgery, radiotherapy and chemotherapy. In this study, we aim to early detect DM in patients with STS using their PET-CT data.

Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation.

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, the performance of existing automated methods for this challenging task is low.

Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation.

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale labelled training data. In medical imaging, these large labelled datasets are sparse, mainly related to the complexity in manual annotation. Deep convolutional neural networks (CNNs), with transferable knowledge, have been employed as a solution to limited annotated data through: 1) fine-tuning generic knowledge with a relatively smaller amount of labelled medical imaging data, and 2) learning image representation that is invariant to different domains.

A deep learning technique for imputing missing healthcare data.

Missing data is a frequent occurrence in medical and health datasets. The analysis of datasets with missing data can lead to loss in statistical power or biased results. We address this issue with a novel deep learning technique to impute missing values in health data.

An Automated Framework for Large Scale Retrospective Analysis of Ultrasound Images.

Objective: Large scale retrospective analysis of fetal ultrasound (US) data is important in the understanding of the cumulative impact of antenatal factors on offspring's health outcomes. Although the benefits are evident, there is a paucity of research into such large scale studies as it requires tedious and expensive effort in manual processing of large scale data repositories. This study presents an automated framework to facilitate retrospective analysis of large scale US data repositories.

Convolutional sparse kernel network for unsupervised medical image analysis.

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data.

Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer.

The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications (e.g., detection and segmentation) requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT.

Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks.

Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasound images. Current approaches to automatic classification are limited to the use of either image patches (cropped images) or the global (whole) image. As many fetal organs have similar visual features, cropped images can misclassify certain structures such as the kidneys and abdomen.

A direct volume rendering visualization approach for serial PET-CT scans that preserves anatomical consistency.

Purpose: Our aim was to develop an interactive 3D direct volume rendering (DVR) visualization solution to interpret and analyze complex, serial multi-modality imaging datasets from positron emission tomography-computed tomography (PET-CT).

Methods: Our approach uses: (i) a serial transfer function (TF) optimization to automatically depict particular regions of interest (ROIs) over serial datasets with consistent anatomical structures; (ii) integration of a serial segmentation algorithm to interactively identify and track ROIs on PET; and (iii) parallel graphics processing unit (GPU) implementation for interactive visualization.

Results: Our DVR visualization more easily identifies changes in ROIs in serial scans in an automated fashion and parallel GPU computation which enables interactive visualization.

Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns.

Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over traditional approaches for cell event detection due to their ability to capture more discriminative features of cellular processes. Supervised deep learning methods, however, are inherently limited due to the scarcity of annotated data.

Controlled Synthesis of Dermoscopic Images via a New Color Labeled Generative Style Transfer Network to Enhance Melanoma Segmentation.

Dermoscopic imaging is an established technique to detect, track, and diagnose malignant melanoma, and one of the ways to improve this technique is via computer-aided image segmentation. Image segmentation is an important step towards building computerized detection and classification systems by delineating the area of interest, in our case, the skin lesion, from the background. However, current segmentation techniques are hard pressed to account for color artifacts within dermoscopic images that are often incorrectly detected as part of the lesion.

Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning.

Tumor histopathology is a crucial step in cancer diagnosis which involves visual inspection of imaging data to detect the presence of tumor cells among healthy tissues. This manual process can be time-consuming, error-prone, and influenced by the expertise of the pathologist. Recent deep learning methods for image classification and detection using convolutional neural networks (CNNs) have demonstrated marked improvements in the accuracy of a variety of medical imaging analysis tasks.

Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks.

Objective: Segmentation of skin lesions is an important step in the automated computer aided diagnosis of melanoma. However, existing segmentation methods have a tendency to over- or under-segment the lesions and perform poorly when the lesions have fuzzy boundaries, low contrast with the background, inhomogeneous textures, or contain artifacts. Furthermore, the performance of these methods are heavily reliant on the appropriate tuning of a large number of parameters as well as the use of effective preprocessing techniques, such as illumination correction and hair removal.

An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification.

The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer vision offer the opportunity for image-based diagnosis, teaching, and biomedical research. However, the content and semantics of an image can vary depending on its modality and as such the identification of image modality is an important preliminary step. The key challenge for automatically classifying the modality of a medical image is due to the visual characteristics of different modalities: some are visually distinct while others may have only subtle differences.

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images.

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion.

Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies.

[F]-Fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) scans of lymphoma patients usually show disease involvement as foci of increased radiotracer uptake. Existing methods for detecting abnormalities, model the characteristics of these foci; this is challenging due to the inconsistent shape and localization information about the lesions. Thresholding the degree of FDG uptake is the standard method to separate different sites of involvement.

Automatic Measurement of Thalamic Diameter in 2-D Fetal Ultrasound Brain Images Using Shape Prior Constrained Regularized Level Sets.

We derived an automated algorithm for accurately measuring the thalamic diameter from 2-D fetal ultrasound (US) brain images. The algorithm overcomes the inherent limitations of the US image modality: nonuniform density; missing boundaries; and strong speckle noise. We introduced a "guitar" structure that represents the negative space surrounding the thalamic regions.

Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram.

'Visibility' is a fundamental optical property that represents the observable, by users, proportion of the voxels in a volume during interactive volume rendering. The manipulation of this 'visibility' improves the volume rendering processes; for instance by ensuring the visibility of regions of interest (ROIs) or by guiding the identification of an optimal rendering view-point. The construction of visibility histograms (VHs), which represent the distribution of all the visibility of all voxels in the rendered volume, enables users to explore the volume with real-time feedback about occlusion patterns among spatially related structures during volume rendering manipulations.