AIMC Topic: Deep Learning

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SSCA-Net: Simultaneous Self- and Channel-Attention Neural Network for Multiscale Structure-Preserving Vessel Segmentation.

BioMed research international
Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels. Unlike existing rule- and conventional learning-based techniques, which hardl...

KG2Vec: A node2vec-based vectorization model for knowledge graph.

PloS one
Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks,...

Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration.

NeuroImage
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear regi...

Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease.

Atherosclerosis
BACKGROUND AND AIMS: Although plaque characterization by intravascular ultrasound (IVUS) is important for risk stratification, frame-by-frame analysis of a whole vascular segment is time-consuming. The aim was to develop IVUS-based algorithms for cla...

A Cascade-SEME network for COVID-19 detection in chest x-ray images.

Medical physics
PURPOSE: The worldwide spread of the SARS-CoV-2 virus poses unprecedented challenges to medical resources and infection prevention and control measures around the world. In this case, a rapid and effective detection method for COVID-19 can not only r...

Balancing Data on Deep Learning-Based Proteochemometric Activity Classification.

Journal of chemical information and modeling
In silico analysis of biological activity data has become an essential technique in pharmaceutical development. Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand-target activity p...

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT.

Oncoimmunology
The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered f...

Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study.

BMC medicine
BACKGROUND: Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional ...

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification.

PLoS computational biology
High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in...