Mr. David D. D. Liang Profile

David D. D. Liang

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Publications (4)

SPIE Journal Paper | 9 July 2024

Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions

Daniel Liang, David Liang, Marc J. Pomeroy, Yongfeng Gao, Licheng Kuo, Lihong Li

JMI, Vol. 11, Issue 04, 044501, (July 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.4.044501

KEYWORDS: Feature extraction, Image classification, Computed tomography, Tissues, Polyps, Machine learning, In vivo imaging, X-ray computed tomography, Medical imaging, Computer aided detection

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PurposeMedical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.ApproachThree feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions’ tissue pathological reports were used as the learning labels.ResultsExperiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value.ConclusionsThe feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.

Proceedings Article | 3 April 2024 Presentation + Paper

An investigation on the individual performance of feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomography screening-detected lesions

Daniel Liang, David Liang, Marc Pomeroy, Yongfeng Gao, Lihong Li, Licheng Kuo

Proceedings Volume 12927, 129271L (2024) https://doi.org/10.1117/12.3006622

KEYWORDS: Feature extraction, Computed tomography, Polyps, Image classification, Tissues, Machine learning, X-ray computed tomography, Deep learning, Computer aided detection, Random forests

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Proceedings Article | 7 April 2023 Poster + Paper

An investigation on energy spectral information of computed tomography for machine learning in lesion classification

Daniel Liang, David Liang, Alice Wei, Ryan Kuo, Shaojie Chang, Marc Pomeroy, Yongfeng Gao

Proceedings Volume 12465, 124652S (2023) https://doi.org/10.1117/12.2654401

KEYWORDS: Computer aided detection, Tissues, X-rays, Polyps, X-ray computed tomography, 3D modeling, Machine learning, Principal component analysis

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Recent advancement of spectral computed tomography (SpCT) technologies by either multi-energy spectral data acquisition with energy-integration detector or single-energy spectral data acquisition with photon counting detector has enabled the reconstruction of virtual monochromatic images (VMIs) at any energy values within and outside the energy spectral ranges of current CTs’ X-ray tubes, resulting in the possibility of not only visualizing the tissue contrast variation characteristics along the X-ray energy dimension, but also quantifying the variation characteristics by machine learning (ML) for prediction of lesion malignancy or computer-aided diagnosis (CADx). This study explored the energy spectral information of SpCT, i.e., the contrast variation characteristics along the X-ray energy dimension, for ML-CADx of lesion type of colorectal polyps. Particularly, the tissue contrast variation patterns, called energy spectral features, along the Xray energy dimension in the VMIs is investigated. A figure of merit (FOM) for the task of ML-CADx is proposed, which ranks the series of VMIs along the X-ray energy dimension by inputting each VMI into a single channel deep learning (DL) pipeline and generating a corresponding a score of AUC (area under the curve of receiver operating characteristics). Then the FOM selects different numbers of the most highly ranked VMIs as the inputs to a multi-channel DL pipeline to generate the corresponding of AUC scores until all VMIs are selected. It is hypothesized that the AUC scores from the multi-channel DL pipeline will increase to reach the highest score and then drop along the ranking order, because all VMIs have the same anatomic structure and, therefore, the strong data redundancy. The FOM reaches the highest AUC score by minimizing the redundancy. We tested the hypothesis by comparing the proposed FOM-rank ML-CADx with the widely used Karhunen-Loève (KL) transform-based ranking method where the principal components are ordered automatically by the KL transform. The lesion data include the CT images of colorectal polyps and the pathological reports after they were resected. The proposed FOM-rank method outperformed the KL-based ranking method with an optimal gain of 4.7%, showing its effectiveness in prediction of lesion malignancy.

Proceedings Article | 16 March 2020 Paper

Performance investigation of deep learning vs. classifier for polyp differentiation via texture features

David Liang, David Wang, Alice Wei, Yeseul Choi, Shu Zhang, Marc Pomeroy, Perry Pickhardt

Proceedings Volume 11314, 113143B (2020) https://doi.org/10.1117/12.2550007

KEYWORDS: Data modeling, Colorectal cancer, Computer aided diagnosis and therapy, Feature extraction, Cancer, Performance modeling, Computed tomography, 3D modeling, Image classification, Colon

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