Ciencias Exactas y Ciencias de la Salud

Now showing 1 - 2 of 2

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 Monterrey
This 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, Sharib
This 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.

Ciencias Exactas y Ciencias de la Salud

Browse

Filters

Settings

Sort By

Results per page

Search Results