A Biologically Motivated, Proto-Object-Based Audiovisual Saliency Model

Ramenahalli, Sudarshan

doi:10.3390/ai1040030

Cited by 3 publications

(2 citation statements)

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“…This method measures the spatial support of a particular class in each image via a heatmap. Saliency maps have been used for region-of-interest extraction [ 47 ], medical imaging [ 48 , 49 ], robot vision [ 50 ], and audio-visual integration [ 51 , 52 ], in addition to AD diagnosis based on MRI [ 32 ]. Saliency maps are obtained by computing the gradient of the output category in relation to the input image.…”

Section: Methodsmentioning

confidence: 99%

Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning‐Based Alzheimer’s Disease Diagnosis

Lopes

Cassani

Falk

2023

Computational Intelligence and Neuroscience

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Biomarkers based on resting-state electroencephalography (EEG) signals have emerged as a promising tool in the study of Alzheimer’s disease (AD). Recently, a state-of-the-art biomarker was found based on visual inspection of power modulation spectrograms where three “patches” or regions from the modulation spectrogram were proposed and used for AD diagnostics. Here, we propose the use of deep neural networks, in particular convolutional neural networks (CNNs) combined with saliency maps, trained on power modulation spectrogram inputs to find optimal patches in a data-driven manner. Experiments are conducted on EEG data collected from fifty-four participants, including 20 healthy controls, 19 patients with mild AD, and 15 moderate-to-severe AD patients. Five classification tasks are explored, including the three-class problem, early-stage detection (control vs. mild-AD), and severity level detection (mild vs. moderate-to-severe). Experimental results show the proposed biomarkers outperform the state-of-the-art benchmark across all five tasks, as well as finding complementary modulation spectrogram regions not previously seen via visual inspection. Lastly, experiments are conducted on the proposed biomarkers to test their sensitivity to age, as this is a known confound in AD characterization. Across all five tasks, none of the proposed biomarkers showed a significant relationship with age, thus further highlighting their usefulness for automated AD diagnostics.

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Section: Methodsmentioning

confidence: 99%

Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning‐Based Alzheimer’s Disease Diagnosis

Lopes

Cassani

Falk

2023

Computational Intelligence and Neuroscience

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“…Quick memorization of encountered configurations can efficiently seed the process of subsequent grinding-in. It has been demonstrated that already at a rudimentary level of proto-objects, the linear combination of visual and auditory feature conspicuity maps, when incorporating gestalt-properties of proximity, convexity, and surroundedness, allows to capture a higher number of valid salient events than uni-sensory saliency maps [35,36] (4,5/D,E). The combined effect of bottom-up activation and top-down bias in living beings has been found in many experiments, e.g., during visual search [37,38].…”

Section: /C)mentioning

confidence: 99%

The Ouroboros Model, Proposal for Self-Organizing General Cognition Substantiated

Thomsen

2021

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The Ouroboros Model has been proposed as a biologically-inspired comprehensive cognitive architecture for general intelligence, comprising natural and artificial manifestations. The approach addresses very diverse fundamental desiderata of research in natural cognition and also artificial intelligence, AI. Here, it is described how the postulated structures have met with supportive evidence over recent years. The associated hypothesized processes could remedy pressing problems plaguing many, and even the most powerful current implementations of AI, including in particular deep neural networks. Some selected recent findings from very different fields are summoned, which illustrate the status and substantiate the proposal.

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