RESUMO
Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance.
Assuntos
Conjuntos de Dados como Assunto/normas , Aprendizado Profundo/normas , Interpretação de Imagem Radiográfica Assistida por Computador/normas , Radiografia Torácica/normas , Viés , Feminino , Humanos , Masculino , Padrões de Referência , Fatores SexuaisRESUMO
When engaged in a conversation, one receives auditory information from the other's speech but also from their own speech. However, this information is processed differently by an effect called Speech-Induced Suppression. Here, we studied brain representation of acoustic properties of speech in natural unscripted dialogues, using electroencephalography (EEG) and high-quality speech recordings from both participants. Using encoding techniques, we were able to reproduce a broad range of previous findings on listening to another's speech, and achieving even better performances when predicting EEG signal in this complex scenario. Furthermore, we found no response when listening to oneself, using different acoustic features (spectrogram, envelope, etc.) and frequency bands, evidencing a strong effect of SIS. The present work shows that this mechanism is present, and even stronger, during natural dialogues. Moreover, the methodology presented here opens the possibility of a deeper understanding of the related mechanisms in a wider range of contexts.
Assuntos
Eletroencefalografia , Fala , Humanos , Fala/fisiologia , Estimulação Acústica/métodos , Eletroencefalografia/métodos , Encéfalo , Mapeamento Encefálico/métodosRESUMO
Cystic echinococcosis is a zoonotic infection caused by the larval stage of Echinococcus granulosus sensu lato. The disease is characterized by the long-term growth of cysts, most commonly in the liver and lungs. Although an ideal model of cystic echinococcosis should induce the development of cysts in the liver and imitate the natural infection route, the murine model of intraperitoneal is still widely used in the field of experimental theraphy. The aim of the present work was to evaluate the usefulness of the murine model of hepatic CE for preclinical drug trials. The effectiveness of albendazole could also be assessed by measuring the diameter of the hepatic cyst. The albendazole significantly reduced the size of the cysts. The ultrastructural alterations of the germinal layer of hepatic cysts provoked by albendazole coincided with those observed in the intraperitoneal model. Similar results were obtained with both albendazole doses. Therefore, the efficacy of albendazole nanocrystals in the murine model of hepatic cystic echinococcosis was carried out at albendazole doses of 25 mg/kg. The abdominal ultrasound allows us to assess the response of cysts to drugs only in a qualitative manner. Although the size of cysts in the albendazole nanocrystal group was not significantly lower than that observed with albendazole, at the ultrastructural level, a greater extent of damage was observed. The murine model of hepatic cystic echinococcosis can be effectively used for assessing the effect of novel formulations or compounds. The main advantage of this model is that cysts are located in the orthotopic organ, which resembles the location most commonly found in human cases. In future studies, the usefulness of the model for pharmacokinetics studies in hepatic cysts will be evaluated.
Assuntos
Albendazol , Modelos Animais de Doenças , Equinococose Hepática , Echinococcus granulosus , Nanopartículas , Albendazol/farmacologia , Albendazol/uso terapêutico , Animais , Camundongos , Nanopartículas/química , Equinococose Hepática/tratamento farmacológico , Equinococose Hepática/parasitologia , Echinococcus granulosus/efeitos dos fármacos , Avaliação Pré-Clínica de Medicamentos , Fígado/parasitologia , Fígado/patologia , Anti-Helmínticos/farmacologia , Anti-Helmínticos/uso terapêutico , Anti-Helmínticos/administração & dosagem , Feminino , Camundongos Endogâmicos BALB CRESUMO
Extreme Learning Machines (ELMs) have become a popular tool for the classification of electroencephalography (EEG) signals for Brain Computer Interfaces. This is so mainly due to their very high training speed and generalization capabilities. Another important advantage is that they have only one hyperparameter that must be calibrated: the number of hidden nodes. While most traditional approaches dictate that this parameter should be chosen smaller than the number of available training examples, in this article we argue that, in the case of problems in which the data contain unrepresentative features, such as in EEG classification problems, it is beneficial to choose a much larger number of hidden nodes. We characterize this phenomenon, explain why this happens and exhibit several concrete examples to illustrate how ELMs behave. Furthermore, as searching for the optimal number of hidden nodes could be time consuming in enlarged ELMs, we propose a new training scheme, including a novel pruning method. This scheme provides an efficient way of finding the optimal number of nodes, making ELMs more suitable for dealing with real time EEG classification problems. Experimental results using synthetic data and real EEG data show a major improvement in the training time with respect to most traditional and state of the art ELM approaches, without jeopardising classification performance and resulting in more compact networks.
Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Projetos de PesquisaRESUMO
OBJECTIVE: This paper tackles the cross-sessions variability of electroencephalography-based brain-computer interfaces (BCIs) in order to avoid the lengthy recalibration step of the decoding method before every use. METHODS: We develop a new approach of domain adaptation based on optimal transport to tackle brain signal variability between sessions of motor imagery BCIs. We propose a backward method where, unlike the original formulation, the data from a new session are transported to a calibration session, and thereby avoiding model retraining. Several domain adaptation approaches are evaluated and compared. We simulated two possible online scenarios: i) block-wise adaptation and ii) sample-wise adaptation. In this study, we collect a dataset of 10 subjects performing a hand motor imagery task in 2 sessions. A publicly available dataset is also used. RESULTS: For the first scenario, results indicate that classifier retraining can be avoided by means of our backward formulation yielding to equivalent classification performance as compared to retraining solutions. In the second scenario, classification performance rises up to 90.23% overall accuracy when the label of the indicated mental task is used to learn the transport. Adaptive time is between 10 and 80 times faster than the other methods. CONCLUSIONS: The proposed method is able to mitigate the cross-session variability in motor imagery BCIs. SIGNIFICANCE: The backward formulation is an efficient retraining-free approach built to avoid lengthy calibration times. Thus, the BCI can be actively used after just a few minutes of setup. This is important for practical applications such as BCI-based motor rehabilitation.
Assuntos
Interfaces Cérebro-Computador , Encéfalo , Eletroencefalografia/métodos , Humanos , Aprendizagem , Aprendizado de MáquinaRESUMO
Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a "natural" way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-participant dataset acquired under this and two others related paradigms, recorded with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.
Assuntos
Interfaces Cérebro-Computador , Percepção da Fala , Inteligência Artificial , Encéfalo , Eletroencefalografia , HumanosRESUMO
Machine learning systems influence our daily lives in many different ways. Hence, it is crucial to ensure that the decisions and recommendations made by these systems are fair, equitable, and free of unintended biases. Over the past few years, the field of fairness in machine learning has grown rapidly, investigating how, when, and why these models capture, and even potentiate, biases that are deeply rooted not only in the training data but also in our society. In this Commentary, we discuss challenges and opportunities for rigorous posterior analyses of publicly available data to build fair and equitable machine learning systems, focusing on the importance of training data, model construction, and diversity in the team of developers. The thoughts presented here have grown out of the work we did, which resulted in our winning the annual Research Parasite Award that GigaSciencesponsors.