Overview of convolutional neural networks architectures for brain tumor segmentation

Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset

APLIKASI PELAYANAN DAN PENYEDIA INFORMASI BERBASIS CHATBOT MENGGUNAKAN DEEP LEARNING DI UNIVERSITAS ISLAM MAJAPAHIT

Di Universitas Islam Majapahit, informasi bagi calon mahasiswa bisa didapatkan dengan cara datang ke kantor pelayanan informasi. Masalahnya, kantor pelayanan informasi memiliki keterbatasan jam dan hari kerja. Tentu menyulitkan bagi calon mahasiswa yang ingin mendapatkan informasi secara cepat. Aplikasi chatbot ini hadir sebagai salah satu solusi digunakan sebagai media layanan kepada siapa saja yang membutuhkan informasi secara cepat dan akurat. Siapapun bisa mengakses aplikasi kapanpun, dimanapun tanpa perlu khawatir terbatas pada jam dan hari kerja, dan mendapat respon yang diinginkan secara cepat. Menggunakan salah satu metode deep learning RNN (Recurrent Neural Network) yang merupakan jaringan saraf tiruan iteratif yang pemrosesannya dipanggil berulang kali untuk memproses input yang biasanya berupa data sekuensial.yang dan didukung oleh algoritma optimasi SGD (Stochastic Gradient Descent). Hasilnya model hidden layer 1 dengan 64 neuron dan hidden layer 2 32 neuron, trainingnya menghasilkan akurasi 93%, model 32x32 89% dan model 64x64 memiliki tingkat akurasi tertinggi dengan 94%

Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Diseño, implementación y evaluación de un sistema de detección y seguimiento de la pose tridimensional de personas basado en “Deep Learning”

En el presente trabajo se va a abordar el tema de la detección de las articulaciones y la pose tridimensional del cuerpo humano en el contexto de la valoración funcional de personas. El objetivo final sería evaluar el grado de dependencia, discapacidad y/o limitaciones que las personas puedan llegar a tener, en particular las de avanzada edad, al realizar actividades de la vida diaria, con especial énfasis en las actividades básicas, como pueden ser: comer, lavarse los dientes, sentarse, limpiar, etc. Con esto se pretende ayudar a los terapeutas ocupacionales a detectar limitaciones de forma temprana y obtener valoraciones objetivas a través del sistema automático, eliminando la subjetividad del personal evaluador y las interferencias que la presencia de este pueda ejercer en las personas que están siendo evaluadas. En el trabajo se estudiarán y aportarán algoritmos que proporcionen parámetros de destreza y funcionalidad. Para ello se han analizado las redes neuronales y las posibles arquitecturas que se podrían aplicar para resolver el problema mencionado. A tal efecto, se ha indagado en las redes que sean capaces de estimar la posición tridimensional de las articulaciones del cuerpo humano a partir de imágenes de profundidad y en RGB, con el fin de evaluar funcionalmente a las personas y obtener una valoración clínica valida.This project will address the issue of joint detection and the three-dimensional pose of the human body in the context of the functional assessment of people. The final objective would be to evaluate the degree of dependence, disability and/or limitations that people may have, particularly the elderly, when performing activities of daily living, with special emphasis on basic activities, such as: eating, brushing teeth, sitting, cleaning, etc. With this we aim to help occupational therapists to detect limitations early and obtain objective evaluations through the automatic system, eliminating the subjectivity of the evaluating personnel and the interferences that the presence of the latter may exert on the people being evaluated. This proyect will study and provide algorithms that offer dexterity and functionality parameters. For this purpose, neural networks and the possible architectures that could be applied to solve the aforementioned problem has been analyzed. To this end, we have investigated networks capable of estimating the three-dimensional position of the joints of the human body from depth and RGB images, in order to functionally evaluate people and obtain a valid clinical assessment.Máster Universitario en Ingeniería de Telecomunicación (M125