Fibro-CoSANet: pulmonary fibrosis prognosis prediction using a convolutional self attention network

Nazi, Zabir Al; Mashrur, Fazla Rabbi; Islam, Amirul; Saha, Shumit

doi:10.1088/1361-6560/ac36a2

Cited by 19 publications

(7 citation statements)

References 21 publications

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“…The type of classification system used in a BCI system is mostly determined by the application's nature and location. With the recent application of deep learning (DL) in different domain (Mashrur et al, 2019 , 2021a ; Nazi et al, 2021 ), Teo et al ( 2017 ) showed the subjects 3D virtual jewelry objects, asked to rate them on a Likert scale, and then categorized EEG signals using deep learning. Again, Aldayel et al ( 2020 ) emphasized the need of spectral valence features to improve prediction accuracy and the merging of classifiers using deep learning to extract features.…”

Section: Introductionmentioning

confidence: 99%

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Mashrur

Rahman

Miya

et al. 2022

Front. Hum. Neurosci.

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Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

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Section: Introductionmentioning

confidence: 99%

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Mashrur

Rahman

Miya

et al. 2022

Front. Hum. Neurosci.

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“…Experiments were run on the OSIC Pulmonary Fibrosis Progression Challenge Benchmark Dataset, 34 the most popular dataset to train models for predicting patients’ severity of decline in lung function. Fibro-CoSANet extracts visual features from CT scans also by means of an attention layer, and combines such features with other clinical data 32 . Then, the prediction of the FVC slope is performed by using regression.…”

Section: Ai Methods In Diagnosis and Prognosis Of Ildsmentioning

confidence: 99%

Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis

et al. 2023

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Interstitial lung disease (ILD) is now diagnosed by an ILD-board consisting of radiologists, pulmonologists, and pathologists. They discuss the combination of computed tomography (CT) images, pulmonary function tests, demographic information, and histology and then agree on one of the 200 ILD diagnoses. Recent approaches employ computer-aided diagnostic tools to improve detection of disease, monitoring, and accurate prognostication. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. This review summarises and highlights the strengths and weaknesses of the latest and most significant published methods that could lead to a holistic system for ILD diagnosis. We explore current AI methods and the data use to predict the prognosis and progression of ILDs. It is then essential to highlight the data that holds the most information related to risk factors for progression, e.g., CT scans and pulmonary function tests. This review aims to identify potential gaps, highlight areas that require further research, and identify the methods that could be combined to yield more promising results in future studies.

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“…Researchers typically consider factors such as image complexity, edge conditions, and uncertainty. When selecting samples, priority is given to images containing crucial structures, lesion regions [22] , or regions that are difficult to segment [23] , as these samples are indispensable for enhancing the model's capability to adapt to intricate scenarios. Considering the specialized nature of the medical image segmentation task, those samples that are important in clinical practice can be selected by combining medical expertise [24] .…”

Section: The Information Selectionmentioning

confidence: 99%

FR-nnUNet: A Novel Medical Segmentation Network Based on the Fuzzy Regions Recognition Scheme and Improved nnU-Net

Huang,

Wang,

Han

et al. 2024

Preprint

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The performance of the traditional supervised learning based medical image segmentation methods heavily depends on the expensive and scarce data labeling. The semi-supervised based segmentation strategies, such as the mean teacher model, have achieved milestone success in this domain. However, few studies have focused on the problem of target selection. Therefore, a novel medical image segmentation model is proposed based on the fuzzy regions recognition scheme and improved nnU-Net. The novel fuzzy regions recognition strategy is introduced and the improved nnU-Net with the new semi-supervised mean teacher network architecture is constructed, which can efficiently optimize the representation of image features via the intensity and spatial transformations. The class-conditional labeling noise identification method is applied to the image segmentation task and combines the concepts of confusion matrix and thresholding to improve the robustness of the model to class imbalance and overconfidence predictions. Experimental results show that the proposed fuzzy selective mean teacher model can achieve excellent segmentation performance on the Left Atrium and ACDC datasets. Compared with the traditional methods, it can not only improve the objective segmentation evaluation metrics in terms of the Dice coefficient, Jaccard coefficient, and Hausdorff95 distance but also obtain better segmentation results especially when less labeled data is used.

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Fibro-CoSANet: pulmonary fibrosis prognosis prediction using a convolutional self attention network

Cited by 19 publications

References 21 publications

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis

FR-nnUNet: A Novel Medical Segmentation Network Based on the Fuzzy Regions Recognition Scheme and Improved nnU-Net

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