Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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https://doi.org/10.1007/978-3-7091-9977-0s therein. However, accurate brain tumor diagnosis is challenging because: 1) data is corrupted with noise, 2) data is relatively scarce, 3) patient representation is unbalanced, i.e., the number of spectra per patient varies, 4) visual inspection of the MRS data suffers from large inter-rater varia

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Zug-, Druck- und Scherfestigkeit,S), a rare heart rhythm disease, from the electrocardiogram (ECG) time series. First, the model is assessed on the ECG classification of type-1 BrS. Then, we define a method to interpret the BrS prediction through Gradient-weighted Class Activation Mapping (Grad-CAM) applied to continuous time serie

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Hartmut Bossel,Walter Heil,Alfred Puckon. As an ophthalmic emergency, BK and FK need to be treated correctly as soon as possible to prevent irrecoverable damage to vision, but the early correct diagnosis between them is challenging. Some research have shown that even trained ophthalmologists have less than 80% accuracy in correctly diag

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,Beispiel für die Anwendung der Ergebnisse,ival rates. Because the bilateral breasts of the same patient are similar and symmetrical, information fusion of bilateral mammogram images is advantageous in improving the detection rate of masses. However, existing mass detection methods use pixel-level corresponding feature fusion methods, which

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Zufallsschwingungen linearer Systeme, important for both diagnosis and treatment of cardiac diseases. Existing automatic segmentation methods for cardiac MRI are based on U-shaped network structure introducing global pooling, attention and etc. operations to extract more effective features. These approaches, however, suffer from a mism

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