Towards Integrative Machine Learning and Knowledge Extraction

Holzinger, Andreas; Goebel, Randy; Palade, Vasile; Ferri, Massimo

doi:10.1007/978-3-319-69775-8_1

Cited by 14 publications

(8 citation statements)

References 39 publications

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“…This is manifested in the HCI-KDD approach, which fosters integrative ML, i.e. a synergistic combination of diverse methodological approaches in a concerted effort to augment human intelligence with artificial intelligence, and eventually to enable what neither of them could do on their own (Holzinger, 2012(Holzinger, , 2013(Holzinger, , 2017Holzinger et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

What do we need to build explainable AI systems for the medical domain?

Holzinger,

Biemann,

Pattichis

et al. 2017

Preprint

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151

145

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Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. However, the central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles of such models they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation (GDPR and ISO/IEC 27001) entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. This is beneficial, e.g. for general understanding, for teaching, for learning, for research, and it can be helpful in court. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.

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Section: Introductionmentioning

confidence: 99%

What do we need to build explainable AI systems for the medical domain?

Holzinger,

Biemann,

Pattichis

et al. 2017

Preprint

Self Cite

151

145

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“…e Dropout layer comes after the second pooling layer. e Part Affinity Fields (PAFs) [37,38] are adopted to predict all the human body key points in the images.…”

Section: Extraction Algorithm Of Human Skeleton Movementsmentioning

confidence: 99%

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Jin

Wen

Xie

2022

Computational Intelligence and Neuroscience

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The paper aims to apply the deep learning-based image visualization technology to extract, recognize, and analyze human skeleton movements and evaluate the effect of the deep learning-based human-computer interaction (HCI) system. Dance education is researched. Firstly, the Visual Geometry Group Network (VGGNet) is optimized using Convolutional Neural Network (CNN). Then, the VGGNet extracts the human skeleton movements in the OpenPose database. Secondly, the Long Short-Term Memory (LSTM) network is optimized and recognizes human skeleton movements. Finally, an HCI system for dance education is designed based on the extraction and recognition methods of human skeleton movements. Results demonstrate that the highest extraction accuracy is 96%, and the average recognition accuracy of different dance movements is stable. The effectiveness of the proposed model is verified. The recognition accuracy of the optimized F-Multiple LSTMs is increased to 88.9%, suitable for recognizing human skeleton movements. The dance education HCI system’s interactive accuracy built by deep learning-based visualization technology reaches 92%; the overall response time is distributed between 5.1 s and 5.9 s. Hence, the proposed model has excellent instantaneity. Therefore, the deep learning-based image visualization technology has enormous potential in human movement recognition, and combining deep learning and HCI plays a significant role.

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“…Classification of disease subtypes, subjects, brain regions, and gradings are often based on ML approaches via automatically segmenting brain MRI data [24]. Making use of such databases, ML not only helps in (semi-)automatically segmenting images, but it is also a tool for trying to answer several research questions, for example predicting tumor growth [28] or investigating minimal tumor burden and therapy resistance by cancer patients [48,49].…”

Section: Databases For Ai/mlmentioning

confidence: 99%

Developments in AI and Machine Learning for Neuroimaging

O’Sullivan

Jeanquartier

Jean-Quartier

et al. 2020

Lecture Notes in Computer Science

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This paper reviews guidelines on how medical imaging analysis can be enhanced by Artificial Intelligence (AI) and Machine Learning (ML). In addition to outlining current and potential future developments, we also provide background information on chemical imaging and discuss the advantages of Explainable AI. We hypothesize that it is a matter of AI to find an invariably recurring parameter that has escaped human attention (e.g. due to noisy data). There is great potential in AI to illuminate the feature space of successful models.

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Towards Integrative Machine Learning and Knowledge Extraction

Cited by 14 publications

References 39 publications

What do we need to build explainable AI systems for the medical domain?

What do we need to build explainable AI systems for the medical domain?

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Developments in AI and Machine Learning for Neuroimaging

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