Multi-instance Learning for Bipolar Disorder Diagnosis using Weakly Labelled Speech Data

Ren, Zhao; Cummins, Nicholas; Kong, Qiuqiang; Plumbley, Mark D.; Schuller, Björn

doi:10.1145/3357729.3357743

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Similarly, Ren et al [45] proposed a MIL approach for bipolar disorder diagnosis by weakly labelled speech data. More recently, in the facial landmark expression-based approach from [46], the authors utilized both feature manipulation and MIL to handle the coarse-grained labels contained in the video clips and got a state-of-the-art performance.…”

Section: Weak Supervision and Multiple Instance Learningmentioning

confidence: 99%

Dual-Stream Multiple Instance Learning for Depression Detection With Facial Expression Videos

Shangguan

Liu

Li³

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Depression is a common mental illness which has brought great harm to the individuals. With recent evidence that many objective physiological signals are associated with depression, automated detection of depression is urgent and important for the growing concern of mental illness. We investigate the problem of classifying depression by facial expressions, which may aid in online diagnosis and rehabilitation engineering of depression. In this work, We propose a weakly supervised learning approach employing multiple instance learning (MIL) on 150 videos data from 75 depressed and 75 healthy subjects.In addition, we present a novel MIL dual-stream aggregator that considers both the instance-level and the bag-level in order to emphasize the information with symptoms. Specifically, our method named ADDMIL uses max-pooling at the instance level to capture symptom information and further integrates the contribution of each instance at the bag level using attention weights. Our method achieves 74.7% accuracy and 74.5% recall on the collected dataset, which not only improves 10.1% accuracy and 9.8% recall over the baseline but also exceeds the best accuracy result of MIL-based method by 2.1%. Our work achieves results that are comparable to the state-of-the-art methods and demonstrates that multiple instance learning has great potential for depression classification. We present for the first time a weakly supervised learning approach in the detection of depression through raw facial expressions, which may provide a new framework for other psychiatric disorders detection methods.

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Section: Weak Supervision and Multiple Instance Learningmentioning

confidence: 99%

Dual-Stream Multiple Instance Learning for Depression Detection With Facial Expression Videos

Shangguan

Liu

Li³

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The basic feasibility of speech-based disease detection or disease/symptom severity prediction could already be demonstrated for a wide spectrum of medical conditions ranging from acute or chronic respiratory diseases, such as cold and flu ( 34 ), COVID-19 ( 24 ), or asthma ( 23 ), via psychiatric disorders, such as anxiety disorder ( 21 ), bipolar disorder ( 22 ), or depression ( 28 ), to developmental disorders, such as autism spectrum disorder ( 30 ), and neurodegenerative diseases, such as Alzheimer's disease ( 20 ) or Parkinson's disease ( 32 ). Promising results in most of the presented studies suggest that AI-based speech analysis might really have the potential to make a valuable contribution to future healthcare.…”

Section: Automatic Speech-based Disease Detectionmentioning

confidence: 99%

Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell

Milling¹,

Pokorny²,

Bartl-Pokorny³

et al. 2022

Front. Digit. Health

Self Cite

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In recent years, advancements in the field of artificial intelligence (AI) have impacted several areas of research and application. Besides more prominent examples like self-driving cars or media consumption algorithms, AI-based systems have further started to gain more and more popularity in the health care sector, however whilst being restrained by high requirements for accuracy, robustness, and explainability. Health-oriented AI research as a sub-field of digital health investigates a plethora of human-centered modalities. In this article, we address recent advances in the so far understudied but highly promising audio domain with a particular focus on speech data and present corresponding state-of-the-art technologies. Moreover, we give an excerpt of recent studies on the automatic audio-based detection of diseases ranging from acute and chronic respiratory diseases via psychiatric disorders to developmental disorders and neurodegenerative disorders. Our selection of presented literature shows that the recent success of deep learning methods in other fields of AI also more and more translates to the field of digital health, albeit expert-designed feature extractors and classical ML methodologies are still prominently used. Limiting factors, especially for speech-based disease detection systems, are related to the amount and diversity of available data, e. g., the number of patients and healthy controls as well as the underlying distribution of age, languages, and cultures. Finally, we contextualize and outline application scenarios of speech-based disease detection systems as supportive tools for health-care professionals under ethical consideration of privacy protection and faulty prediction.

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“…Implementation of the manifold learning scheme is also witnessed to offer significant detection of mental disorders Liu et al [14]. When learning is carried out over multiple instances, the chances of effective diagnosis are further ascertained Ren et al [15]. However, these studies introduce a model which requires the system to be configured and fine-tuned on the basis of fed input.…”

Section: Related Workmentioning

confidence: 99%

Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder

Kumar¹,

Saxena²

2022

IJECE

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In the present state of health and wellness, mental illness is always deemed less importance compared to other forms of physical illness. In reality, mental illness causes serious multi-dimensional adverse effect to the subject with respect to personal life, social life, as well as financial stability. In the area of mental illness, bipolar disorder is one of the most prominent type which can be triggered by any external stimulation to the subject suffering from this illness. There diagnosis as well as treatment process of bipolar disorder is very much different from other form of illness where the first step of impediment is the correct diagnosis itself. According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. Which is characterized by specific mood associated with depression and mania. However, there is no study associated with mixed-mood episode detection which is characterized by combination of various symptoms of bipolar disorder in random, unpredictable, and uncertain manner. Hence, the model contributes to obtain granular information with dynamics of mood transition. The simulated outcome of the proposed system in MATLAB shows that resulting model is capable enough for detection of mixed mood episode precisely

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Multi-instance Learning for Bipolar Disorder Diagnosis using Weakly Labelled Speech Data

Cited by 12 publications

References 32 publications

Dual-Stream Multiple Instance Learning for Depression Detection With Facial Expression Videos

Dual-Stream Multiple Instance Learning for Depression Detection With Facial Expression Videos

Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell

Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder

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