AIMC Topic: Neural Networks, Computer

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Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN).

Journal of medical systems
Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the br...

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation.

IEEE transactions on medical imaging
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive archite...

On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment.

IEEE transactions on medical imaging
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context o...

From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior.

Neural computation
The study of neuronal interactions is at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, and the Brainome) that attempt to obtain a detailed map of the entire brain. Under...

Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping.

Medical image analysis
A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored...

Task definition, annotated dataset, and supervised natural language processing models for symptom extraction from unstructured clinical notes.

Journal of biomedical informatics
INTRODUCTION: Machine learning (ML) and natural language processing have great potential to improve information extraction (IE) within electronic medical records (EMRs) for a wide variety of clinical search and summarization tools. Despite ML advance...

Artificial intelligence and neural networks in urology: current clinical applications.

Minerva urologica e nefrologica = The Italian journal of urology and nephrology
INTRODUCTION: As we enter the era of "big data," an increasing amount of complex health-care data will become available. These data are often redundant, "noisy," and characterized by wide variability. In order to offer a precise and transversal view ...

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.

International journal of molecular sciences
Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecu...

Identification of Similar Chinese Congou Black Teas Using an Electronic Tongue Combined with Pattern Recognition.

Molecules (Basel, Switzerland)
It is very difficult for humans to distinguish between two kinds of black tea obtained with similar processing technology. In this paper, an electronic tongue was used to discriminate samples of seven different grades of two types of Chinese Congou b...