Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Automatic detection and segmentation of prostate cancer using deep learning techniques(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-20) Quihui Rubio, Pablo César; González Mendoza, Miguel; puemcuervo, emimayorquin; Alfaro Ponce, Mariel; Mata Miquel, Christian; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoProstate cancer is a major cause of death among men worldwide, and detecting it usually involves invasive procedures. Magnetic resonance imaging (MRI) has become a common research area for detecting this cancer because it represents a less invasive option. However, segmenting the prostate gland from MRI images can be a complicated task that requires expert opinions, which is both time-consuming and inconsistent. This thesis proposes a novel deep-learning architecture to automate and obtain accurate and reliable segmentation of the prostate gland in MRI scans. Precise segmentation is crucial for radiotherapy planning, as it determines the tumor’s location and size, which affects treat- ment effectiveness and reduces radiation exposure to surrounding healthy tissues. Therefore, a thorough comparison between architectures from the state-of-the-art is also performed. Convolutional neural networks have shown great potential in medical image segmenta- tion, but the uncertainty associated with their predictions is often overlooked. Therefore, this work proposes a novel approach incorporating uncertainty quantification to ensure reliable and trustworthy results. The models were evaluated on a dataset of prostate T2-MRI scans obtained in collab- oration with the Centre Hospitalarie Dijon and Universitat Politecnica de Catalunya. The results showed that the proposed architecture FAU-Net outperforms most existing models in the literature, with an improvement of 5% in the Dice Similarity Coefficient (DSC) and In- tersection over Union (IoU). However, the best model overall was R2U-Net, which achieved segmentation accuracy and uncertainty estimation values of 85% and 76% for DSC and IoU, respectively, with an uncertainty score lower than 0.05. In addition to the proposed model and comparison between models for prostate seg- mentation and uncertainty quantification, a web application was presented for easier access to the trained models in a clinical setting. This web app would allow medical professionals to upload MRI scans of prostate cancer patients and obtain accurate and reliable segmentation quickly and easily. This would reduce the time and effort required for manual segmentation and improve patient outcomes by facilitating better treatment planning. Overall, this work presents a novel strategy for prostate segmentation using deep learn- ing models and uncertainty quantification. The proposed method provides a reliable and trust- worthy segmentation while quantifying the uncertainty associated with the predictions. This research can benefit prostate cancer patients by improving treatment planning and outcomes.
- TYolov5: A Temporal Yolov5 detector based on quasi-recurrent neural networks for real-time handgun detection in video(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12-01) Duran Vega, Mario Alberto; GONZALEZ MENDOZA, MIGUEL; 123361; González Mendoza, Miguel; puemcuervo; Ochoa Ruiz, Gilberto; Morales González Quevedo, Annette; Sánchez Castellanos, Héctor Manuel; School of Engineering and Science; Campus Monterrey; Chang Fernández, LeonardoTimely handgun detection is a crucial problem to improve public safety; nevertheless, the effectiveness of many surveillance systems, still depend of finite human attention. Much of the previous research on handgun detection is based on static image detectors, leaving aside valuable temporal information that could be used to improve object detection in videos. To improve the performance of surveillance systems, a real-time temporal handgun detection system should be built. Using Temporal Yolov5, an architecture based in Quasi-Recurrent Neural Networks, temporal information is extracted from video to improve the results of the handgun detection. Moreover, two publicity available datasets are proposed, labeled with hands, guns, and phones. One containing 2199 static images to train static detectors, and another with 5960 frames of videos to train temporal modules. Additionally, we explore two temporal data augmentation techniques based in Mosaic and Mixup. The resulting systems are three real-time architectures: one focused in reducing inference with a mAP(50:95) of 56.1, another in having a good balance between inference and accuracy with a mAP(50:95) of 59.4, and a last one specialized in accuracy with a mAP(50:95) of 60.6. Temporal Yolov5 achieves real-time detection and take advantage of temporal features contained in videos to perform better than Yolov5 in our temporal dataset. Making TYolov5 suitable for real-world applications.