Net2Vis – A Visual Grammar for Automatically Generating Publication-Tailored CNN Architecture Visualizations

Bäuerle, Alex; Onzenoodt, Christian van; Ropinski, Timo

doi:10.1109/tvcg.2021.3057483

Cited by 49 publications

(17 citation statements)

References 41 publications

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“…Furthermore, the artificial neural network can be used to evaluate the accuracy of predicted model with divided training and validation datasets ( Curchoe et al, 2020 ). Currently, there are some useful visualization and analysis tools for neural networks, such as NeuralNetTools ( Beck, 2018 ), spiking neuronal networks ( Galindo et al, 2020 ), and Net2Vis ( Bauerle et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

She

Diao

et al. 2022

Front. Genet.

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Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.

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Section: Introductionmentioning

confidence: 99%

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

She

Diao

et al. 2022

Front. Genet.

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“…The third Conv layer’s output is flattened into a one-dimensional vector and fed through a fully connected hidden layer with 8 nodes, used together with the output neuron with the “sigmoid” activation function for final frame-level classification. ( a ) Block diagram representation; ( b ) Layer diagram representation using Net2Vis [ 33 ].…”

Section: Figurementioning

confidence: 99%

Using Voice Activity Detection and Deep Neural Networks with Hybrid Speech Feature Extraction for Deceptive Speech Detection

Mihalache

Burileanu

2022

Sensors

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In this work, we first propose a deep neural network (DNN) system for the automatic detection of speech in audio signals, otherwise known as voice activity detection (VAD). Several DNN types were investigated, including multilayer perceptrons (MLPs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), with the best performance being obtained for the latter. Additional postprocessing techniques, i.e., hysteretic thresholding, minimum duration filtering, and bilateral extension, were employed in order to boost performance. The systems were trained and tested using several data subsets of the CENSREC-1-C database, with different simulated ambient noise conditions, and additional testing was performed on a different CENSREC-1-C data subset containing actual ambient noise, as well as on a subset of the TIMIT database. An accuracy of up to 99.13% was obtained for the CENSREC-1-C datasets, and 97.60% for the TIMIT dataset. We proceed to show how the final VAD system can be adapted and employed within an utterance-level deceptive speech detection (DSD) processing pipeline. The best DSD performance is achieved by a novel hybrid CNN-MLP network leveraging a fusion of algorithmically and automatically extracted speech features, and reaches an unweighted accuracy (UA) of 63.7% on the RLDD database, and 62.4% on the RODeCAR database.

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“…Then, a global average pooling layer and a final layer with 2 neurons and softmax activation were added, representing the classification classes: benign vs. malignant. A schematic view of the architecture, generated with a visual grammar tool [59], is provided in Figure 2.…”

Section: Deep Learning Architecturementioning

confidence: 99%

Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

et al. 2021

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(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

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Net2Vis – A Visual Grammar for Automatically Generating Publication-Tailored CNN Architecture Visualizations

Cited by 49 publications

References 41 publications

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

Using Voice Activity Detection and Deep Neural Networks with Hybrid Speech Feature Extraction for Deceptive Speech Detection

Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

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