AIMC Topic: Deep Learning

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TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

PLoS computational biology
Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations...

HOReID: Deep High-Order Mapping Enhances Pose Alignment for Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Despite the remarkable progress in recent years, person Re-Identification (ReID) approaches frequently fail in cases where the semantic body parts are misaligned between the detected human boxes. To mitigate such cases, we propose a novel High-Order ...

An Accurate and Lightweight Method for Human Body Image Super-Resolution.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we propose a new method to super-resolve low resolution human body images by learning efficient multi-scale features and exploiting useful human body prior. Specifically, we propose a lightweight multi-scale block (LMSB) as basic modul...

Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning.

Nature methods
Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extra...

Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging.

Journal of neuroscience methods
BACKGROUND: Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation.

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemb...

Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we ap...

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation.

Sensors (Basel, Switzerland)
Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya f...

Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns.

Biomolecules
Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful metho...