2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Reliable prostate cancer detection using ultrasound imaging can significantly improve patient out... more Reliable prostate cancer detection using ultrasound imaging can significantly improve patient outcomes and survival. One outstanding challenge for developing machine learning models that can accurately detect cancer is labeling the ultrasound data based on histopathology reports, where only a coarse label is provided for an entire biopsy sample. As a result, training a model for pixel level analysis of ultrasound images with high rate of label noise leads to model overfitting the noisy data and poor generalization performance. To address this challenge, we leverage the deep ensemble technique for uncertainty estimation to detect out-of-distribution inputs and data with noisy labels. We use Masksembles idea to overcome the computational cost associated with the ensemble techniques. We further improve the performance of the model by using co-teaching framework with a regularized loss. We demonstrate that the proposed method helps with the overconfident predictions of learning models, and noisy nature of the labels data.
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using... more Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using a systematic and nontargeted approach are the high rate of false negatives and not being patient specific. Intraprostatic pathology information of individuals is not available during the biopsy procedure. Even after histopathology analysis of the biopsy cores, the report only represents a statistical distribution of cancer within the core. Labeling the data based on these noisy labels results in challenges for network training, where networks inevitably overfit to noisy data. To overcome this problem, we argue that it is critical to build a clean dataset. In this paper, we address the challenges associated with using statistical labels and alleviate this issue by taking advantage of confident learning to estimate uncertainty in the data label. Next, we find the label error, clean the labels, and evaluate the clean data by comparing it using a metric based on the involvement of cancer in core.
International Journal of Computer Assisted Radiology and Surgery, 2022
Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by... more Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels. However, this introduces significant amount of label noise to training and degrades the classification performance. Previously, we suggested that histopathology-reported cancer involvement can be a reasonable approximation for the label noise. Here, we propose an involvement-based label refinement (iLR) method to correct corrupted labels and improve cancer classification. The difference between predicted and true cancer involvements is used to guide the label refinement process. We further incorporate iLR into state-of-the-art methods for learning with noisy labels and predicting cancer involvement. We use 258 biopsy cores from 70 patients and demonstrate that our proposed label refinement method improves the performance of multiple noise-tolerant approaches and achieves a balanced accuracy, correlation coefficient, and mean absolute error of 76.7%, 0.68, and 12.4, respectively. Our key contribution is to leverage a data-centric method to deal with noisy labels using histopathology reports, and improve the performance of prostate cancer diagnosis through a hierarchical training process with label refinement.
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Temporal enhanced ultrasound (TeUS) is a tissue characterization approach based on analysis of a ... more Temporal enhanced ultrasound (TeUS) is a tissue characterization approach based on analysis of a temporal series of US data. Previously we demonstrated that intrinsic or external micro-motions of scatterers in the tissue contribute towards the tissue classification properties of TeUS. This property is beneficial to detect early stage cancer, for example, where changes in nuclei configuration (scatteres) dominate tissue properties. In this study, we propose an analytical derivation and experiments to acquire TeUS through manipulation of US imaging parameters, which may be simpler to translate to clinical applications. The feasibility of the proposed method is demonstrated on tissue-mimicking phantoms. Using an autoencoder classifier, we are able to classify phantoms of varying elasticities and scattering sizes.
Tissue-mimicking phantoms can be used to study various diagnostic imaging techniques and image-gu... more Tissue-mimicking phantoms can be used to study various diagnostic imaging techniques and image-guided therapeutic interventions. Bioprinting enables the incorporation of live cells into printed phantoms. Some advantages of bioprinted phantoms include their close similarity to in vivo condition and change in phantom composition with time as the cells proliferate and secrete the extra cellular matrix components. In this study, we 3D-print alginate to form three different types of phantoms; those containing human vascular smooth muscle cells, human liver cancer cells, and no cells, each representing benign tissue, cancer tissue, and controls, respectively. The phantoms are imaged with a clinical ultrasound scanner and Temporal Enhanced Ultrasound (TeUS) data is collected. The comparison of the power spectrum of TeUS depicts separation among the three phantom types.
Introduction and overall goal: Prostate Cancer (PCa) detection under transrectal ultrasound (TRUS... more Introduction and overall goal: Prostate Cancer (PCa) detection under transrectal ultrasound (TRUS) guidance is blind to intraprostatic pathology, and can miss clinically significant disease. Multi-parametric MRI (mp-MRI) and its fusion with TRUS show promising results to target potential cancer lesions. However, mp-MRI has a high false positive rate, and can miss smaller foci of high-grade disease [1]. Ultrasoundbased tissue typing has been investigated extensively to enable more accurate targeted biopsy [2]. Rationale and background: Our group has been investigating a new ultrasound (US)-based paradigm for tissue characterization referred to as Temporal Enhanced Ultrasound (TeUS) [2]. TeUS is defined as the time-series of US frames captured by insonation of a fixed tissue location over time. We have previously demonstrated that spectral analysis of TeUS data can be deployed for detection of PCa in US images [2]. Specific aim: In this work, we aim to explicitly model the temporal re...
International Journal of Computer Assisted Radiology and Surgery, 2021
Purpose Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to... more Purpose Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false negatives. Development of a machine-learning model that can reliably identify suspicious cancer regions is highly desirable. However, the models proposed to-date do not consider the uncertainty present in their output or the data to benefit clinical decision making for targeting biopsy. Methods We propose a deep network for improved detection of prostate cancer in systematic biopsy considering both the label and model uncertainty. The architecture of our model is based on U-Net, trained with temporal enhanced ultrasound (TeUS) data. We estimate cancer detection uncertainty using test-time augmentation and test-time dropout. We then use uncertainty metrics to report the cancer probability for regions with high confidence to help the clinical decision making during the biopsy procedure. Results Experiments for prostate cancer classification includes data from 183 prostate biopsy cores of 41 patients. We achieve an area under the curve, sensitivity, specificity and balanced accuracy of 0.79, 0.78, 0.71 and 0.75, respectively. Conclusion Our key contribution is to automatically estimate model and label uncertainty towards enabling targeted ultrasound-guided prostate biopsy. We anticipate that such information about uncertainty can decrease the number of unnecessary biopsy with a higher rate of cancer yield.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020
Ultrasound is a common imaging modality used for targeting suspected cancerous tissue in prostate... more Ultrasound is a common imaging modality used for targeting suspected cancerous tissue in prostate biopsy. Since ultrasound images have very low specificity and sensitivity for visualizing the cancer foci, a significant body of literature have aimed to develop ultrasound tissue characterization solutions to alleviate this issue. Major challenges are the substantial heterogeneity in data, and the noisy, limited number of labeled data available from pathology of biopsy samples. A recently proposed tissue characterization method uses spectral analysis of time series of ultrasound data taken during the biopsy procedure combined with deep networks. However, the real-value transformations in these networks neglect the phase information of the signal. In this paper, we study the importance of phase information and compare different ways of extracting reliable features including complex neural networks. These networks can help with analyzing the phase information to use the full capacity of the data. Our results show that the phase content can stabilize training specially with non-stationary time series. The proposed approach is generic and can be applied to several other scenarios where the phase information is important and noisy labels are present.
Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography, 2010
Non destructive testing requires the predetermination of the parameters of the experiment. In thi... more Non destructive testing requires the predetermination of the parameters of the experiment. In this paper the focus is on the design of the experiments for infrared testing based on analytical models. Resulting design parameters are obtained using conservative approximate equations. The results are particularly relevant for non destructive testing in thin plates of various materials
2011 19th Mediterranean Conference on Control & Automation (MED), 2011
Index Terms—Non destructive testing; Fault diagnosis; Ex- periment design; Parameters predetermin... more Index Terms—Non destructive testing; Fault diagnosis; Ex- periment design; Parameters predetermination; Modeling & simulation. Abstract—We analyse heat conduction in a slab with imposed persistent oscillating temperature. Fourier series and periodic time varying solutions are derived by using separation of variables techniques. The analysis is aimed at assessing the limits of applicability of the simplified analytical model derived in (1) under the hypothesis of semi-infinite spatial domain. Such model allows for parameters predetermination in the design of experiments related with nondestructive tests for defects detection and failure prediction. The effect of the finite length of the specimen is investigated, therefore setting the foundation for experimental investigation of nondestructive damage detec- tion with infrared active thermography with boundary effects accounted for. simple formula that relates the frequency of the excitation with the amplitude of the temperature profile at a certain point in the domain. In this paper we want to asses the limitations of such simplified model by deriving analyti- cal series solutions of the initial-boundary-values problem associated with the heat conduction in a finite bar with one extreme exposed to periodic time varying excitation. Findings of the simplified model in (1) are also compared with numerical solutions obtained with the commercial finite element software ANSYS TM . Closed form expressions to evaluate the error introduced by discarding the finite length of the domain are derived. The analytical analysis is carried out by (i) using Fourier series projection which accounts for the transient time solution, and (ii) by assuming that the steady-state solution in the time domain due to a purely periodical persistent excitation is periodical with amplitude characterized by the properties of the space differential operator in the governing equation. Analytical models are useful in assessing the validity of the simplified model in (1), and to design non destructive testing experiments when the semi-infinite domain hypothesis is not applicable. The rest of the paper is organized as follows. In Section II we derive analytical solutions that can be used for design of our experimental setup, and to assess the validity range and limitations of the model in (1). Comparisons between the analytical and the numerical solutions in finite domain versus the solution in the semi-infinite domain are shown in Section III, and the effect of the finite length of the domain in the experimental setup is investigated. Conclusions and future research directions are discussed in Section IV. II. ANALYTICAL INVESTIGATION IN A FINITE DOMAIN
The International Journal of Advanced Manufacturing Technology, 2011
ABSTRACT This paper describes the development of an approach for trajectory planning of cable-sus... more ABSTRACT This paper describes the development of an approach for trajectory planning of cable-suspended parallel robots using optimal control approach. A prototype has been built, and tests have been carried out to verify the theoretical results. This paper briefly illustrates this device and presents some initial tests. The final dynamic equations are organized in a closed form similar to serial manipulator equations. Dynamic load-carrying capacity problem is converted into a trajectory optimization problem which is fundamentally a constrained nonlinear optimization problem. The problem is formulated using optimal control theory, and the ideas are analyzed using Pontryagin’s minimum principle. The optimal solutions are found by solving the corresponding nonlinear two-point boundary value problems. The main objective is to find the manipulator load-carrying capacity in point-to-point task by considering actuator torque while cable forces are positive.
IEEE transactions on medical imaging, Jan 25, 2018
Temporal Enhanced Ultrasound (TeUS), comprising the analysis of variations in backscattered signa... more Temporal Enhanced Ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this manuscript, we propose to use deep Recurrent Neural Networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that Long Short-Term Memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98 and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Reliable prostate cancer detection using ultrasound imaging can significantly improve patient out... more Reliable prostate cancer detection using ultrasound imaging can significantly improve patient outcomes and survival. One outstanding challenge for developing machine learning models that can accurately detect cancer is labeling the ultrasound data based on histopathology reports, where only a coarse label is provided for an entire biopsy sample. As a result, training a model for pixel level analysis of ultrasound images with high rate of label noise leads to model overfitting the noisy data and poor generalization performance. To address this challenge, we leverage the deep ensemble technique for uncertainty estimation to detect out-of-distribution inputs and data with noisy labels. We use Masksembles idea to overcome the computational cost associated with the ensemble techniques. We further improve the performance of the model by using co-teaching framework with a regularized loss. We demonstrate that the proposed method helps with the overconfident predictions of learning models, and noisy nature of the labels data.
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using... more Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using a systematic and nontargeted approach are the high rate of false negatives and not being patient specific. Intraprostatic pathology information of individuals is not available during the biopsy procedure. Even after histopathology analysis of the biopsy cores, the report only represents a statistical distribution of cancer within the core. Labeling the data based on these noisy labels results in challenges for network training, where networks inevitably overfit to noisy data. To overcome this problem, we argue that it is critical to build a clean dataset. In this paper, we address the challenges associated with using statistical labels and alleviate this issue by taking advantage of confident learning to estimate uncertainty in the data label. Next, we find the label error, clean the labels, and evaluate the clean data by comparing it using a metric based on the involvement of cancer in core.
International Journal of Computer Assisted Radiology and Surgery, 2022
Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by... more Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels. However, this introduces significant amount of label noise to training and degrades the classification performance. Previously, we suggested that histopathology-reported cancer involvement can be a reasonable approximation for the label noise. Here, we propose an involvement-based label refinement (iLR) method to correct corrupted labels and improve cancer classification. The difference between predicted and true cancer involvements is used to guide the label refinement process. We further incorporate iLR into state-of-the-art methods for learning with noisy labels and predicting cancer involvement. We use 258 biopsy cores from 70 patients and demonstrate that our proposed label refinement method improves the performance of multiple noise-tolerant approaches and achieves a balanced accuracy, correlation coefficient, and mean absolute error of 76.7%, 0.68, and 12.4, respectively. Our key contribution is to leverage a data-centric method to deal with noisy labels using histopathology reports, and improve the performance of prostate cancer diagnosis through a hierarchical training process with label refinement.
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Temporal enhanced ultrasound (TeUS) is a tissue characterization approach based on analysis of a ... more Temporal enhanced ultrasound (TeUS) is a tissue characterization approach based on analysis of a temporal series of US data. Previously we demonstrated that intrinsic or external micro-motions of scatterers in the tissue contribute towards the tissue classification properties of TeUS. This property is beneficial to detect early stage cancer, for example, where changes in nuclei configuration (scatteres) dominate tissue properties. In this study, we propose an analytical derivation and experiments to acquire TeUS through manipulation of US imaging parameters, which may be simpler to translate to clinical applications. The feasibility of the proposed method is demonstrated on tissue-mimicking phantoms. Using an autoencoder classifier, we are able to classify phantoms of varying elasticities and scattering sizes.
Tissue-mimicking phantoms can be used to study various diagnostic imaging techniques and image-gu... more Tissue-mimicking phantoms can be used to study various diagnostic imaging techniques and image-guided therapeutic interventions. Bioprinting enables the incorporation of live cells into printed phantoms. Some advantages of bioprinted phantoms include their close similarity to in vivo condition and change in phantom composition with time as the cells proliferate and secrete the extra cellular matrix components. In this study, we 3D-print alginate to form three different types of phantoms; those containing human vascular smooth muscle cells, human liver cancer cells, and no cells, each representing benign tissue, cancer tissue, and controls, respectively. The phantoms are imaged with a clinical ultrasound scanner and Temporal Enhanced Ultrasound (TeUS) data is collected. The comparison of the power spectrum of TeUS depicts separation among the three phantom types.
Introduction and overall goal: Prostate Cancer (PCa) detection under transrectal ultrasound (TRUS... more Introduction and overall goal: Prostate Cancer (PCa) detection under transrectal ultrasound (TRUS) guidance is blind to intraprostatic pathology, and can miss clinically significant disease. Multi-parametric MRI (mp-MRI) and its fusion with TRUS show promising results to target potential cancer lesions. However, mp-MRI has a high false positive rate, and can miss smaller foci of high-grade disease [1]. Ultrasoundbased tissue typing has been investigated extensively to enable more accurate targeted biopsy [2]. Rationale and background: Our group has been investigating a new ultrasound (US)-based paradigm for tissue characterization referred to as Temporal Enhanced Ultrasound (TeUS) [2]. TeUS is defined as the time-series of US frames captured by insonation of a fixed tissue location over time. We have previously demonstrated that spectral analysis of TeUS data can be deployed for detection of PCa in US images [2]. Specific aim: In this work, we aim to explicitly model the temporal re...
International Journal of Computer Assisted Radiology and Surgery, 2021
Purpose Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to... more Purpose Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false negatives. Development of a machine-learning model that can reliably identify suspicious cancer regions is highly desirable. However, the models proposed to-date do not consider the uncertainty present in their output or the data to benefit clinical decision making for targeting biopsy. Methods We propose a deep network for improved detection of prostate cancer in systematic biopsy considering both the label and model uncertainty. The architecture of our model is based on U-Net, trained with temporal enhanced ultrasound (TeUS) data. We estimate cancer detection uncertainty using test-time augmentation and test-time dropout. We then use uncertainty metrics to report the cancer probability for regions with high confidence to help the clinical decision making during the biopsy procedure. Results Experiments for prostate cancer classification includes data from 183 prostate biopsy cores of 41 patients. We achieve an area under the curve, sensitivity, specificity and balanced accuracy of 0.79, 0.78, 0.71 and 0.75, respectively. Conclusion Our key contribution is to automatically estimate model and label uncertainty towards enabling targeted ultrasound-guided prostate biopsy. We anticipate that such information about uncertainty can decrease the number of unnecessary biopsy with a higher rate of cancer yield.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020
Ultrasound is a common imaging modality used for targeting suspected cancerous tissue in prostate... more Ultrasound is a common imaging modality used for targeting suspected cancerous tissue in prostate biopsy. Since ultrasound images have very low specificity and sensitivity for visualizing the cancer foci, a significant body of literature have aimed to develop ultrasound tissue characterization solutions to alleviate this issue. Major challenges are the substantial heterogeneity in data, and the noisy, limited number of labeled data available from pathology of biopsy samples. A recently proposed tissue characterization method uses spectral analysis of time series of ultrasound data taken during the biopsy procedure combined with deep networks. However, the real-value transformations in these networks neglect the phase information of the signal. In this paper, we study the importance of phase information and compare different ways of extracting reliable features including complex neural networks. These networks can help with analyzing the phase information to use the full capacity of the data. Our results show that the phase content can stabilize training specially with non-stationary time series. The proposed approach is generic and can be applied to several other scenarios where the phase information is important and noisy labels are present.
Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography, 2010
Non destructive testing requires the predetermination of the parameters of the experiment. In thi... more Non destructive testing requires the predetermination of the parameters of the experiment. In this paper the focus is on the design of the experiments for infrared testing based on analytical models. Resulting design parameters are obtained using conservative approximate equations. The results are particularly relevant for non destructive testing in thin plates of various materials
2011 19th Mediterranean Conference on Control & Automation (MED), 2011
Index Terms—Non destructive testing; Fault diagnosis; Ex- periment design; Parameters predetermin... more Index Terms—Non destructive testing; Fault diagnosis; Ex- periment design; Parameters predetermination; Modeling & simulation. Abstract—We analyse heat conduction in a slab with imposed persistent oscillating temperature. Fourier series and periodic time varying solutions are derived by using separation of variables techniques. The analysis is aimed at assessing the limits of applicability of the simplified analytical model derived in (1) under the hypothesis of semi-infinite spatial domain. Such model allows for parameters predetermination in the design of experiments related with nondestructive tests for defects detection and failure prediction. The effect of the finite length of the specimen is investigated, therefore setting the foundation for experimental investigation of nondestructive damage detec- tion with infrared active thermography with boundary effects accounted for. simple formula that relates the frequency of the excitation with the amplitude of the temperature profile at a certain point in the domain. In this paper we want to asses the limitations of such simplified model by deriving analyti- cal series solutions of the initial-boundary-values problem associated with the heat conduction in a finite bar with one extreme exposed to periodic time varying excitation. Findings of the simplified model in (1) are also compared with numerical solutions obtained with the commercial finite element software ANSYS TM . Closed form expressions to evaluate the error introduced by discarding the finite length of the domain are derived. The analytical analysis is carried out by (i) using Fourier series projection which accounts for the transient time solution, and (ii) by assuming that the steady-state solution in the time domain due to a purely periodical persistent excitation is periodical with amplitude characterized by the properties of the space differential operator in the governing equation. Analytical models are useful in assessing the validity of the simplified model in (1), and to design non destructive testing experiments when the semi-infinite domain hypothesis is not applicable. The rest of the paper is organized as follows. In Section II we derive analytical solutions that can be used for design of our experimental setup, and to assess the validity range and limitations of the model in (1). Comparisons between the analytical and the numerical solutions in finite domain versus the solution in the semi-infinite domain are shown in Section III, and the effect of the finite length of the domain in the experimental setup is investigated. Conclusions and future research directions are discussed in Section IV. II. ANALYTICAL INVESTIGATION IN A FINITE DOMAIN
The International Journal of Advanced Manufacturing Technology, 2011
ABSTRACT This paper describes the development of an approach for trajectory planning of cable-sus... more ABSTRACT This paper describes the development of an approach for trajectory planning of cable-suspended parallel robots using optimal control approach. A prototype has been built, and tests have been carried out to verify the theoretical results. This paper briefly illustrates this device and presents some initial tests. The final dynamic equations are organized in a closed form similar to serial manipulator equations. Dynamic load-carrying capacity problem is converted into a trajectory optimization problem which is fundamentally a constrained nonlinear optimization problem. The problem is formulated using optimal control theory, and the ideas are analyzed using Pontryagin’s minimum principle. The optimal solutions are found by solving the corresponding nonlinear two-point boundary value problems. The main objective is to find the manipulator load-carrying capacity in point-to-point task by considering actuator torque while cable forces are positive.
IEEE transactions on medical imaging, Jan 25, 2018
Temporal Enhanced Ultrasound (TeUS), comprising the analysis of variations in backscattered signa... more Temporal Enhanced Ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this manuscript, we propose to use deep Recurrent Neural Networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that Long Short-Term Memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98 and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.
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