AIMC Topic: Neural Networks, Computer

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D-TrAttUnet: Toward hybrid CNN-transformer architecture for generic and subtle segmentation in medical images.

Computers in biology and medicine
Over the past two decades, machine analysis of medical imaging has advanced rapidly, opening up significant potential for several important medical applications. As complicated diseases increase and the number of cases rises, the role of machine-base...

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.

Interdisciplinary sciences, computational life sciences
Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising...

A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor.

Sensors (Basel, Switzerland)
Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and ener...

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Scientific reports
Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of variou...

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

International journal of laboratory hematology
INTRODUCTION: Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, alth...

Improving span-based Aspect Sentiment Triplet Extraction with part-of-speech filtering and contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible spans of...

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

Magnetic resonance in medicine
PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.

A unifying framework for functional organization in early and higher ventral visual cortex.

Neuron
A key feature of cortical systems is functional organization: the arrangement of functionally distinct neurons in characteristic spatial patterns. However, the principles underlying the emergence of functional organization in the cortex are poorly un...

Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation.

Medical image analysis
The success of deep learning on image classification and recognition tasks has led to new applications in diverse contexts, including the field of medical imaging. However, two properties of deep neural networks (DNNs) may limit their future use in m...

Vein pattern visualisation for biometric identification with cGAN on a New Zealand dataset.

Forensic science international
Forensic identification using vein patterns in standard colour images presents significant challenges due to their low visibility. Recent efforts have employed various computational techniques, including artificial neural networks and optical vein di...