Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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CellSpot: Deep Learning-Based Efficient Cell Center Detection in Microscopic Images proposed pipeline drastically cuts down on annotation efforts while still delivering commendable performance. By leveraging the proposed method, we aim to enhance efficiency in cell detection, paving the way for more expedient and resource-effective analysis in biological research and medical diagn

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Artificial Neural Networks and Machine Learning – ICANN 202433rd International C

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Isomorphic Fluorescent Nucleoside Analogs,etter than classical machine learning methods. In addition, we show that BiBoNet achieves better results than deep learning models based on individual or combined data. We highlight the importance of multi-omics integration through deep learning for improved medical diagnosis using microbiome and me

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How Good Does a Parent Have to Be?lities of capsule networks and domain generalization techniques to adjust between training subjects and tasks, thereby improving recognition accuracy and algorithm performance in the target domain. The experimental results demonstrate that CapsDA-Net achieves state-of-the-art performance on the SEED

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Designs for Evaluating Behavior Changehe protein functionalities. Extensive experiments demonstrate that ProTeM achieves performance on par with individually finetuned models, and outshines the model based on conventional multi-task learning. Moreover, ProTeM unveils an enhanced capacity for protein representation, surpassing state-of-t