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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- 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.
- Image captioning for automated grading and understanding of pre-cancerous inflammations in ulcerative colitis on endoscopic images(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Valencia Velarde, Flor Helena; Ochoa Ruiz, Gilberto; emimmayorquin; Hinojosa Cervantes, Salvador Miguel; Gonzalez Mendoza, Miguel; School of Engineering and Sciences; Campus Monterrey; Ali, SharibThis thesis presents the development and results of an automated system for grading and understanding ulcerative colitis (UC) through image captioning. UC is a chronic inflammatory disease of the large intestine, characterized by alternating periods of remission and relapse. The conventional method for assessing UC severity involves the Mayo Endoscopic Scoring (MES) system, which depends on the visual evaluation of mucosal characteristics. This method is subjective and can result in considerable variability between different observers. The primary objective of this thesis is to investigate and evaluate contemporary methodologies for developing an image captioning model that can generate MES scores and descriptive captions for mucosal features observed in endoscopic images. This research involved an extensive examination of various convolutional neural networks (CNNs) for visual feature extraction and the implementation of several sequence models for natural language processing (NLP), including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNNs). Our system was rigorously evaluated on a dataset consisting of 982 images obtained from both public repositories and proprietary collections. The combination of DenseNet121 for CNN-based feature extraction and 2 layers GRU for sequence generation yielded the best performance, achieving a BLEU-4 score of 0.7352. This high level of similarity between the reference and predicted captions indicates the model’s effectiveness in accurately capturing and describing critical mucosal features necessary for UC grading. While our system performed well in predicting MES-0 to MES-2 categories, it encountered challenges in accurately predicting MES-3 classifications. This discrepancy is likely due to the underrepresentation of severe cases in the training dataset. Despite this limitation, the system’s ability to generate comprehensive descriptions of mucosal features represents a significant advancement in the automated evaluation of UC. The contributions of this thesis include the creation of a dataset for UC captioning task, a detailed analysis of various CNN architectures and sequence models, an extensive evaluation of their performance, and the development of a robust framework for automated UC grading and description generation. Our findings suggest that combining advanced visual feature extraction techniques with sophisticated NLP models can significantly improve the accuracy and reliability of automated medical diagnosis systems. By reducing inter-observer variability and providing a valuable tool for training new clinicians, this automated grading and captioning system has the potential to enhance diagnostic accuracy and clinical decision-making in UC management. This work represents a substantial step forward in the field of endoscopic imaging, underscoring the importance of integrating machine learning techniques in clinical practice. Additionally, by generating detailed descriptions, this approach helps mitigate the “black box” nature of deep learning, offering more transparency and interpretability in automated medical diagnoses.
- A novel dataset and deep learning method for automatic exposure correction in endoscopic imaging(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12-01) García Vega, Carlos Axel; Falcón Morales, Luis Eduardo; puemcuervo, emipsanchez; Daul, Christian; González Mendoza, Miguel; Roshan Biswal, Rajesh; School of Engineering and Sciences; Campus Estado de México; Ochoa Ruiz, GilbertoEndoscopy is such an important medical practice that one of the most common type of cancer worldwide, cause of many deaths, can be diagnosed and treated since through this imaging technique clinicians can diagnose cancerous lesions in hollow organs. Nonetheless, endo- scopic images are often affected by sudden illumination changes which entail regions with overexposure, underexposure or even both errors, in accordance with the light source pose and the lumen texture of the inner walls. These poor light conditions can carry several negative consequences either for the examination itself or on the performance of Computed-assisted Diagnosis (CAD) or Computed-aided Surgery (CAS). However, almost no effort has been done for deploy endoscopic image enhancement methods that can perform adequately (even when both errors appear simultaneously) and in real-time. The contribution of the present work in overall aims to enhance the quality of Field-of-View (FoV) from endoscopic ex- aminations and Computed-assisted Diagnosis through real-time Deep Learning techniques, however, for achieving this general objective, we first built a reliable reference-based dataset Endo4IE, evaluates and validated by experts, to be an standard dataset for IE purposes, due to the lack of this dataset in the literature. Afterwards, we evaluated IE methods on our dataset to find out a prospect method for our case-of-study, in this case LMSPEC originally introduced to enhance images from natural scenes. We made adaptations over the objective function of the prospect method to obtain better performance regarding to structure and less artifacts in the enhanced frame. Finally, we tested on the Endo4IE dataseta and evaluate with state-of- the-art metrics against the baseline method, thus the proposed implementation has yielded a significant improvement over LMSPEC reaching a SSIM increase of 4.40% and 4.21% for overexposed and underexposed images, respectively. Regarding PSNR, an improvement of 3.83% for over-exposed and just 0.01% below with respect to LMSPEC.
- 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.