| 1. |
- Fan, Peng, et al.
(författare)
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Scatter and crosstalk corrections for (99m)Tc/(123)I dual-radionuclide imaging using a CZT SPECT system with pinhole collimators.
- 2015
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Ingår i: Medical Physics. - : Wiley. - 0094-2405 .- 2473-4209. ; 42:12, s. 6895-6911
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Tidskriftsartikel (refereegranskat)abstract
- The energy spectrum for a cadmium zinc telluride (CZT) detector has a low energy tail due to incomplete charge collection and intercrystal scattering. Due to these solid-state detector effects, scatter would be overestimated if the conventional triple-energy window (TEW) method is used for scatter and crosstalk corrections in CZT-based imaging systems. The objective of this work is to develop a scatter and crosstalk correction method for (99m)Tc/(123)I dual-radionuclide imaging for a CZT-based dedicated cardiac SPECT system with pinhole collimators (GE Discovery NM 530c/570c).
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| 2. |
- Su, Peng, et al.
(författare)
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Scheduling Resource to Deploy Monitors in Automated Driving Systems
- 2023
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Ingår i: Dependable Computer Systems and Networks. - : Springer Nature. ; , s. 285-294
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Konferensbidrag (refereegranskat)abstract
- Deep Neural Networks (DNN) constitute an important technology for operational perception in Automated Driving Systems (ADS). However, the trustworthiness of such DNN is one concern in the system engineering and quality management. Therefore, it is critical to monitor conditions and ensure the safety of the implementations for this advanced technology. One solution is to use Conditional Monitors (CM) to detect possible faults. However, such monitors challenge resource (e.g., data and memory) management of limited memory space in the ADS hardware. This paper proposes a resource scheme for deploying a monitor in ADS by integrating dynamic memory scheduling with Responsibility-Sensitive Safety (RSS). We use the car-following system as a case study to evaluate our scheme. YOLOv5 and KITTI datasets simulate a perception module where various monitors detect faults. We measure the time cost of conventional scheduling pipelines and our method. Compared with the conventional method, our scheme reduces 43.7% of execution time per cycle.
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| 3. |
- Yang, Zhikai, et al.
(författare)
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3D Breast Ultrasound Image Classification Using 2.5D Deep learning
- 2024
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Ingår i: 17th International Workshop on Breast Imaging, IWBI 2024. - : SPIE.
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Konferensbidrag (refereegranskat)abstract
- The 3D breast ultrasound is a radiation-free and effective imaging technology for breast tumor diagnosis. However, checking the 3D breast ultrasound is time-consuming compared to mammograms. To reduce the workload of radiologists, we proposed a 2.5D deep learning-based breast ultrasound tumor classification system. First, we used the pre-trained STU-Net to finetune and segment the tumor in 3D. Then, we fine-tuned the DenseNet-121 for classification using the 10 slices with the biggest tumoral area and their adjacent slices. The Tumor Detection, Segmentation, and Classification on Automated 3D Breast Ultrasound (TDSC-ABUS) MICCAI Challenge 2023 dataset was used to train and validate the performance of the proposed method. Compared to a 3D convolutional neural network model and radiomics, our proposed method has better performance.
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| 4. |
- Yang, Zhikai, et al.
(författare)
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Lesion Localization in Digital Breast Tomosynthesis with Deformable Transformers by Using 2.5D Information
- 2024
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Ingår i: Medical Imaging 2024: Computer-Aided Diagnosis. - : SPIE-Intl Soc Optical Eng.
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Konferensbidrag (refereegranskat)abstract
- In this study, we adapted a transformer-based method to localize lesions in digital breast tomosynthesis (DBT) images. Compared with convolutional neural network-based object detection methods, the transformer-based method does not require non-maximum suppression postprocessing. Integrated deformable convolution detection transformers can better capture small-size lesions. We added transfer learning to tackle the issue of the lack of annotated data from DBT. To validate the superiority of the transformer-based detection method, we compared the results with deep-learning object detection methods. The experimental results demonstrated that the proposed method performs better than all comparison methods.
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