Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry was held on 1–15 July 2021. The scope of this online conference was to gather experts that are well-known worldwide who are currently working in chemical sensor technologies and to provide an online forum for the presention and discussion of new results. Throughout this event, topics of interest included, but were not limited to, the following: electrochemical devices and sensors; optical chemical sensors; mass-sensitive sensors; materials for chemical sensing; nano- and micro-technologies for sensing; chemical assays and validation; chemical sensor applications; analytical methods; gas sensors and apparatuses; electronic noses; electronic tongues; microfluidic devices; lab-on-a-chip; single-molecule sensing; nanosensors; and medico-diagnostic testing.

electrochemical sensors --- carbon nanotubes --- electropolymerization --- gallic acid --- ellagic acid --- syringaldehyde --- vanillin --- cognac and brandy carbon dots --- pH sensing --- active surface preservation --- nanosensor --- fluorescence

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.

interpretable/explainable machine learning --- image classification --- image processing --- machine learning models --- white box --- black box --- cancer prediction --- deep learning --- multimodal learning --- convolutional neural networks --- autism --- fMRI --- texture analysis --- melanoma --- glcm matrix --- machine learning --- classifiers --- explainability --- explainable AI --- XAI --- medical imaging --- diagnosis --- ARMD --- change detection --- unsupervised learning --- microwave breast imaging --- image reconstruction --- tumor detection --- digital pathology --- whole slide image processing --- multiple instance learning --- deep learning classification --- HER2 --- medical images --- transfer learning --- optimizers --- neo-adjuvant treatment --- tumour cellularity --- cancer --- breast cancer --- diagnostics --- imaging --- computation --- artificial intelligence --- 3D segmentation --- active surface --- discriminant analysis --- PET imaging --- medical image analysis --- brain tumor --- cervical cancer --- colon cancer --- lung cancer --- computer vision --- musculoskeletal images --- lung disease detection --- taxonomy --- convolutional neural network --- CycleGAN --- data augmentation --- dermoscopic images --- domain transfer --- macroscopic images --- skin lesion segmentation --- infection detection --- COVID-19 --- X-ray images --- bayesian inference --- shifted-scaled dirichlet distribution --- MCMC --- gibbs sampling --- object detection --- surgical tools --- open surgery --- egocentric camera --- computers in medicine --- segmentation --- MRI --- ECG signal detection --- portable monitoring devices --- 1D-convolutional neural network --- medical image segmentation --- domain adaptation --- meta-learning --- U-Net --- computed tomography (CT) --- magnetic resonance imaging (MRI) --- low-dose --- sparse-angle --- quantitative comparison --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.

interpretable/explainable machine learning --- image classification --- image processing --- machine learning models --- white box --- black box --- cancer prediction --- deep learning --- multimodal learning --- convolutional neural networks --- autism --- fMRI --- texture analysis --- melanoma --- glcm matrix --- machine learning --- classifiers --- explainability --- explainable AI --- XAI --- medical imaging --- diagnosis --- ARMD --- change detection --- unsupervised learning --- microwave breast imaging --- image reconstruction --- tumor detection --- digital pathology --- whole slide image processing --- multiple instance learning --- deep learning classification --- HER2 --- medical images --- transfer learning --- optimizers --- neo-adjuvant treatment --- tumour cellularity --- cancer --- breast cancer --- diagnostics --- imaging --- computation --- artificial intelligence --- 3D segmentation --- active surface --- discriminant analysis --- PET imaging --- medical image analysis --- brain tumor --- cervical cancer --- colon cancer --- lung cancer --- computer vision --- musculoskeletal images --- lung disease detection --- taxonomy --- convolutional neural network --- CycleGAN --- data augmentation --- dermoscopic images --- domain transfer --- macroscopic images --- skin lesion segmentation --- infection detection --- COVID-19 --- X-ray images --- bayesian inference --- shifted-scaled dirichlet distribution --- MCMC --- gibbs sampling --- object detection --- surgical tools --- open surgery --- egocentric camera --- computers in medicine --- segmentation --- MRI --- ECG signal detection --- portable monitoring devices --- 1D-convolutional neural network --- medical image segmentation --- domain adaptation --- meta-learning --- U-Net --- computed tomography (CT) --- magnetic resonance imaging (MRI) --- low-dose --- sparse-angle --- quantitative comparison