| 1 |
Development and diagnostic validation of the Brisbane Evidence-Based Language Test
|
|
|
|
In: Research outputs 2014 to 2021 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Primary and Secondary Progressive Aphasia in Posterior Cortical Atrophy
|
|
|
|
In: Life; Volume 12; Issue 5; Pages: 662 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease
|
|
|
|
In: Brain Sciences; Volume 12; Issue 2; Pages: 146 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going?
|
|
|
|
In: Diagnostics; Volume 12; Issue 4; Pages: 927 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks
|
|
|
|
In: Micromachines; Volume 13; Issue 4; Pages: 501 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Applications of Explainable Artificial Intelligence in Diagnosis and Surgery
|
|
|
|
In: Diagnostics; Volume 12; Issue 2; Pages: 237 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
The Roles of the ADOS-2 and Cognition in Measuring and Diagnosing Autism Spectrum Disorder
|
|
|
|
In: Psychology Dissertations (2022)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Talking about chronic pain in family settings: a glimpse of older persons' everyday realities.
|
|
|
|
In: BMC geriatrics, vol. 22, no. 1, pp. 358 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Reliability of perceptual measurement of Apraxia of Speech characteristics
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
|
|
Zhou, Jiejie; Liu, Yan-Lin; Zhang, Yang; Chen, Jeon-Hor; Combs, Freddie J; Parajuli, Ritesh; Mehta, Rita S; Liu, Huiru; Chen, Zhongwei; Zhao, Youfan; Pan, Zhifang; Wang, Meihao; Yu, Risheng; Su, Min-Ying. - : eScholarship, University of California, 2021
|
|
Abstract:
BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.Materials and methodsA total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.ResultsThe diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A-5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.ConclusionDiagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.
|
|
Keyword:
Biomedical Imaging; breast neoplasms; computer-assisted diagnosis; deep learning; machine learning; magnetic resonance imaging; Oncology and Carcinogenesis
|
|
URL: https://escholarship.org/uc/item/5mj029ws
|
|
BASE
|
|
Hide details
|
|
|
| 11 |
BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Uniform data set language measures for bvFTD and PPA diagnosis and monitoring.
|
|
|
|
In: Alzheimer's & dementia (Amsterdam, Netherlands), vol 13, iss 1 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Social Behavior Observer Checklist: Patterns of Spontaneous Behaviors Differentiate Patients With Neurodegenerative Disease From Healthy Older Adults.
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Uniform data set language measures for bvFTD and PPA diagnosis and monitoring.
|
|
|
|
In: Alzheimer's & dementia (Amsterdam, Netherlands), vol 13, iss 1 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Vocal drum sounds in human beatboxing: An acoustic and articulatory exploration using electromagnetic articulography
|
|
|
|
In: ISSN: 0001-4966 ; EISSN: 1520-8524 ; Journal of the Acoustical Society of America ; https://hal.univ-grenoble-alpes.fr/hal-03107358 ; Journal of the Acoustical Society of America, Acoustical Society of America, 2021, 149 (1), pp.191-206. ⟨10.1121/10.0002921⟩ ; https://asa.scitation.org/doi/full/10.1121/10.0002921 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Synchronous Product of Time Petri Nets and its Applications to Fault-Diagnosis ; Produit Synchrone de Réseaux de Petri temporel et ses Applications au Diagnostic de Fautes
|
|
|
|
In: https://hal.laas.fr/tel-03528121 ; Embedded Systems. INSA de Toulouse, 2021. English. ⟨NNT : 2021ISAT0025⟩ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Preparing Student Teachers of Languages to Promote Plurilingual Competence ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Cross-cultural cognitive assessment of dementia: a meta-analysis of the impact of illiteracy on dementia screening and an evaluation of a transcultural short-term memory assessment ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
