Anomaly detection in medical imaging-a mini review
ME Tschuchnig, M Gadermayr - International Data Science Conference, 2021 - Springer
The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the workload for …
improvements in detecting, visualizing and segmenting lesions, easing the workload for …
[HTML][HTML] Overcoming the challenges in the development and implementation of artificial intelligence in radiology: a comprehensive review of solutions beyond …
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective
clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of …
clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of …
Fast non-Markovian diffusion model for weakly supervised anomaly detection in brain MR images
In medical image analysis, anomaly detection in weakly supervised settings has gained
significant interest due to the high cost associated with expert-annotated pixel-wise labeling …
significant interest due to the high cost associated with expert-annotated pixel-wise labeling …
Unsupervised outlier detection for mixed-valued dataset based on the adaptive k-nearest neighbor global network
Y Wang, X Cao, Y Li - IEEE Access, 2022 - ieeexplore.ieee.org
Outlier detection aims to reveal data patterns different from existing data. Benefit from its
good robustness and interpretability, the outlier detection method for numerical dataset …
good robustness and interpretability, the outlier detection method for numerical dataset …
HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues.
R Mutha, S Lavate, S Limkar… - Soft Computing-A …, 2023 - search.ebscohost.com
Human health issues require estimation of heart rhythm anomalies, brain wave pattern
abnormalities, blood parameter outliers, social media analysis, and more. Researchers …
abnormalities, blood parameter outliers, social media analysis, and more. Researchers …
Anomaly‐based Alzheimer's disease detection using entropy‐based probability Positron Emission Tomography images
Deep neural networks trained on labeled medical data face major challenges owing to the
economic costs of data acquisition through expensive medical imaging devices, expert labor …
economic costs of data acquisition through expensive medical imaging devices, expert labor …
A low redundancy wavelet entropy edge detection algorithm
Fast edge detection of images can be useful for many real-world applications. Edge
detection is not an end application but often the first step of a computer vision application …
detection is not an end application but often the first step of a computer vision application …
Assessing and testing anomaly detection for finding prostate cancer in spatially registered multi-parametric MRI
Background Evaluating and displaying prostate cancer through non-invasive imagery such
as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research …
as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research …
Low-False-Alarm-Rate Timing and Duration Estimation of Noisy Frequency Agile Signal by Image Homogeneous Detection and Morphological Signature Matching …
Frequency hopping spread spectrum (FHSS) applies widely to communication and radar
systems to ensure communication information and channel signal quality by tuning …
systems to ensure communication information and channel signal quality by tuning …
A novel hybrid heuristic adopted ensemble of deep learning models for COVID-19 detection framework using CT and X-Ray images
C Mary Shiba, M Navaneethakrishnan… - Computer Methods in …, 2023 - Taylor & Francis
ABSTRACT In 2019, Corona Virus Disease (COVID)-19 has created an important impact on
people's health and economy because of its rapid spreading. Therefore, the earlier detection …
people's health and economy because of its rapid spreading. Therefore, the earlier detection …