2,399 research outputs found
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works
Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence
or early adulthood. It reduces the life expectancy of patients by 15 years.
Abnormal behavior, perception of emotions, social relationships, and reality
perception are among its most significant symptoms. Past studies have revealed
that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased
volume of white and gray matter can be observed due to this disease. Magnetic
resonance imaging (MRI) is the popular neuroimaging technique used to
explore structural/functional brain abnormalities in SZ disorder, owing to its
high spatial resolution. Various artificial intelligence (AI) techniques have been
employed with advanced image/signal processing methods to accurately diagnose
SZ. This paper presents a comprehensive overview of studies conducted on
the automated diagnosis of SZ using MRI modalities. First, an AI-based computer
aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections
are presented. Then, this section introduces the most important conventional
machine learning (ML) and deep learning (DL) techniques in the diagnosis of
diagnosing SZ. A comprehensive comparison is also made between ML and DL
studies in the discussion section. In the following, the most important challenges
in diagnosing SZ are addressed. Future works in diagnosing SZ using AI
techniques and MRI modalities are recommended in another section. Results,
conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación
(España)/ FEDER under the RTI2018-098913-B100 projectConsejerÃa de EconomÃa, Innovación, Ciencia y Empleo (Junta de AndalucÃa) and
FEDER under CV20-45250 and A-TIC-080-UGR18 project
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