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.
Browse
Search Results
- A prompt assisted image enhancement model using BERT classifier and modified LMSPEC and STTN techniques for endoscopic images(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Cerriteño Magaña, Javier; Ochoa Ruiz, Gilberto; emipsanchez; Sánchez Ante, Gildardo; Alfaro Ponce, Mariel; School of Engineering and Sciences; Campus MonterreyThis document presents a research thesis for the Master in Computer Science (MCCi) degree at Tecnologico de Monterrey. The field of medical imaging, particularly in endoscopy, has seen significant advancements in image enhancement techniques aimed at improving the clarity and interpretability of captured images. Numerous models and methodologies have been developed to enhance medical images, ranging from traditional algorithms to complex deep learning frameworks. However, the effective implementation of these techniques often requires substantial expertise in computer science and image processing, which may pose a barrier for medical professionals who primarily focus on clinical practice. This thesis presents a novel prompt-assisted image enhancement model that integrates the LMSPEC and STTN techniques, augmented by BERT models equipped with added attention blocks. This innovative approach enables medical practitioners to specify desired image enhancements through natural language prompts, significantly simplifying the enhancement process. By interpreting and acting upon user-defined requests, the proposed model not only empowers clinicians with limited technical backgrounds to effectively enhance endoscopic images but also streamlines diagnostic workflows. To the best of our knowledge, this is the first dedicated prompt-assisted image enhancement model specifically tailored for medical imaging applications. Moreover, the architecture of the proposed model is designed with flexibility in mind, allowing for the seamless incorporation of future image enhancement models and techniques as they emerge. This adaptability ensures that the model remains relevant and effective as the field of medical imaging continues to evolve. The results of this research contribute to the ongoing effort to make advanced image processing technologies more accessible to medical professionals, thereby enhancing the quality of care provided to patients through improved diagnostic capabilities.
- A prompt assisted image enhancement model using BERT classifier and modified LMSPEC and STTN techniques for endoscopic images(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Cerriteño Magaña, Javier; Ochoa Ruiz, Gilberto; emimmayorquin; Alfaro Ponce, Mariel; School of Engineering and Sciences; Campus Monterrey; Sánchez Ante, GildardoThis document presents a research thesis for the Master in Computer Science (MCCi) degree at Tecnologico de Monterrey. The field of medical imaging, particularly in endoscopy, has seen significant advancements in image enhancement techniques aimed at improving the clarity and interpretability of captured images. Numerous models and methodologies have been developed to enhance medical images, ranging from traditional algorithms to complex deep learning frameworks. However, the effective implementation of these techniques often requires substantial expertise in computer science and image processing, which may pose a barrier for medical professionals who primarily focus on clinical practice. This thesis presents a novel prompt-assisted image enhancement model that integrates the LMSPEC and STTN techniques, augmented by BERT models equipped with added attention blocks. This innovative approach enables medical practitioners to specify desired image enhancements through natural language prompts, significantly simplifying the enhancement process. By interpreting and acting upon user-defined requests, the proposed model not only empowers clinicians with limited technical backgrounds to effectively enhance endoscopic images but also streamlines diagnostic workflows. To the best of our knowledge, this is the first dedicated prompt-assisted image enhancement model specifically tailored for medical imaging applications. Moreover, the architecture of the proposed model is designed with flexibility in mind, allowing for the seamless incorporation of future image enhancement models and techniques as they emerge. This adaptability ensures that the model remains relevant and effective as the field of medical imaging continues to evolve. The results of this research contribute to the ongoing effort to make advanced image processing technologies more accessible to medical professionals, thereby enhancing the quality of care provided to patients through improved diagnostic capabilities.
- Attention YOLACT++: achieving robust and real-time medical instrument segmentation in endoscopic procedures.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-04) Ángeles Cerón, Juan Carlos; Chang Fernández, Leonardo; 345979; Chang Fernández, Leonardo; emipsanchez; González Mendoza, Miguel; Alí, Sharib; Escuela de Ingeniería y Ciencias; Campus Monterrey; Ochoa Ruiz, GilbertoImage-based tracking of laparoscopic instruments via instance segmentation plays a fundamental role in computer and robotic-assisted surgeries by aiding surgical navigation and increasing patient safety. Despite its crucial role in minimally invasive surgeries, accurate tracking of surgical instruments is a challenging task to achieve because of two main reasons 1) complex surgical environment, and 2) lack of model designs with both high accuracy and speed. Previous attempts in the field have prioritized robust performance over real-time speed rendering them unfeasible for live clinical applications. In this thesis, we propose the use of attention mechanisms to significantly improve the recognition capabilities of YOLACT++, a lightweight single-stage instance segmentation architecture, which we target at medical instrument segmentation. To further improve the performance of the model, we also investigated the use of custom data augmentation, and anchor optimization via a differential evolution search algorithm. Furthermore, we investigate the effect of multi-scale feature aggregation strategies in the architecture. We perform ablation studies with Convolutional Block Attention and Criss-cross Attention modules at different stages in the network to determine an optimal configuration. Our proposed model CBAM-Full + Aug + Anch drastically outperforms the previous state-of-the art in commonly used robustness metrics in medical segmentation, achieving 0.435 MI_DSC and 0.471 MI_NSD while running at 69 fps, which is more than 12 points more robust in both metrics and 14 times faster than the previous best model. To our knowledge, this is the first work that explicitly focuses on both real-time performance and improved robustness.
