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

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,Transformer Based Prototype Learning for?Weakly-Supervised Histopathology Tissue Semantic Segmentatto obtain more complete localization maps. Additionally, we introduce a self-refinement mechanism to dampen the falsely activated regions in the initial localization map. Extensive experiments on two histopathology datasets demonstrate that our proposed model achieves the state-of-the-art performanc

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,A Graph Convolutional Siamese Network for?the?Assessment and?Recognition of?Physical Rehabilitation model reaches state-of-the-art performance on action classification and outperforms the Dynamic Time Warping algorithm and hidden Markov model method by a large margin in terms of assessment accuracy.

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Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay

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https://doi.org/10.1007/978-3-7091-9977-0 of min-, max-, and average-pooling of the features, and 2) a self-attention mechanism. We evaluate the proposed method on multiple neural network architectures in a five-fold leave-patient-out cross-validation scheme and also against human experts on a withheld data set. We find that classification

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Hartmut Bossel,Walter Heil,Alfred Puck87.20%, 83.12%, 0.85 and 0.85 respectively, which has achieved the best effect compared with other classification methods. Furthermore, visualization technique Grad-CAM++ is used to provide interpretability for the validity of our model.

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Zufallsschwingungen linearer Systeme,e dilated convolutions. In order to improve the ability to learn the precise boundary of the objects, a gated boundary-aware branch is introduced and utilized to concentrate on the object border region. The effectiveness and robustness of the network are confirmed by evaluating this method on the AC

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