Titlebook: Artificial Intelligence and Machine Learning for Healthcare; Vol. 1: Image and Da Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain Book 2023 Th

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Polyp Segmentation with Deep Ensembles and Data Augmentation,l rate of this cancer, but this intervention depends on the accurate detection of polys in the surrounding tissues. Missing a poly has serious consequences. One way to guard against human error is to develop automatic polyp detection systems. Deep learning semantic segmentation offers one approach t

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Autistic Verbal Behavior Parameters,expressions are hard to understand for them. Biotech is a research project to get new alternates to help these individuals to communicate. The main goal here is to provide advances and tuned tools through audio and video real-time processing. Part of the previous work in this research stated the bas

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Advances in Modelling Hospital Medical Wards,ts with larger clinical complexity and needs. The patients in medical wards exhibit multiple pathologies, with a burden of activities, risks, and costs for health systems, mining their sustainability. Internal Medicine Departments play an important role in the care of those patients that access the

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BioGNN: How Graph Neural Networks Can Solve Biological Problems,ain feature is the capability of processing graph structured data with minimal loss of structural information. This makes GNNs the ideal family of models for processing a wide variety of biological data: metabolic networks, structural formulas of molecules, and proteins are all examples of biologica

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eCustomer Relationship Management,iction of outcome of patients, etc. Genetic tests can provide prognostic information in breast cancer for both diagnosis and treatment planning. In this study, we developed a radiogenomics method to discover imaging biomarkers on breast MRI for prediction of genetic test results for breast cancer by

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https://doi.org/10.1007/978-3-540-85017-5he process. As an example of the effective synergy between AI and data-driven acquisition/reconstruction in radial MRI, we present a GReedy Adaptive Data-driven Environment (GRADE) for intelligent radial sampling that uses the power spectrum of the reconstructed image and AI-based superresolution st

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https://doi.org/10.1007/978-3-540-85017-5ve proposed domain adaptation-based technique for liver tumor detection in multi-phase CT images. We discuss the domain-shift problem in different phases of multiphase liver CT images and introduce our domain adaptation technique for multi-phase CT images. We have used PV phase images to learn a mod

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eCustomer Relationship Management,eal datasets during network training. The main characteristic of our method, differently from other existing techniques, lies in the generation procedure carried out in multiple steps, based on the intuition that, by splitting the procedure in multiple phases, the overall generation task is simplifi