Ultrasonic tissue characterization for the classification of prostate tissue

2004 ◽  
Vol 115 (5) ◽  
pp. 2586-2586
Author(s):  
Ulrich Scheipers ◽  
Helmut Ermert ◽  
Katharina Koenig ◽  
Hans‐Joerg Sommerfeld ◽  
Miguel Garcia‐Schuermann ◽  
...  
Keyword(s):  
Tissue Characterization ◽  
Prostate Tissue

scholarly journals Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 530 ◽  
Author(s):  
Tuba Yilmaz
Keyword(s):  
Logistic Regression ◽  
Dielectric Property ◽  
Measurement Error ◽  
Tissue Characterization ◽  
Linear Models ◽  
Hepatic Tissue ◽  
Data Set ◽  
Property Data

Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithms’ ability to decrease the measurement error of open-ended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, k-nearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability.

scholarly journals Evaluation of fitting models for prostate tissue characterization using extended‐range b‐factor diffusion‐weighted imaging

2017 ◽  
Vol 79 (4) ◽  
pp. 2346-2358 ◽  
Author(s):  
Fredrik Langkilde ◽  
Thiele Kobus ◽  
Andriy Fedorov ◽  
Ruth Dunne ◽  
Clare Tempany ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Tissue Characterization ◽  
Prostate Tissue ◽  
Diffusion Weighted ◽  
Extended Range

scholarly journals Discovery and Classification of Fusion Transcripts in Prostate Cancer and Normal Prostate Tissue

2015 ◽  
Vol 185 (7) ◽  
pp. 1834-1845 ◽  
Author(s):  
Jian-Hua Luo ◽  
Silvia Liu ◽  
Ze-Hua Zuo ◽  
Rui Chen ◽  
George C. Tseng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Tissue ◽  
Normal Prostate ◽  
Fusion Transcripts ◽  
Normal Prostate Tissue

Multimodal classification of prostate tissue: a feasibility study on combining multiparametric MRI and ultrasound

2015 ◽  
Author(s):  
Hussam Al-Deen Ashab ◽  
Nandinee Fariah Haq ◽  
Guy Nir ◽  
Piotr Kozlowski ◽  
Peter Black ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Multiparametric Mri ◽  
Prostate Tissue ◽  
Multimodal Classification
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