AIMC Topic: Algorithms

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Multi-task neural networks by learned contextual inputs.

Neural networks : the official journal of the International Neural Network Society
This paper explores learned-context neural networks. It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing trainable task parameters. The architecture is interesting due to its powerf...

Mining core information by evaluating semantic importance for unpaired image captioning.

Neural networks : the official journal of the International Neural Network Society
Recently, exciting progress has been made in the research of supervised image captioning. However, manually annotated image-annotation pair data is difficult and expensive to obtain. Therefore, unpaired image captioning becomes an emerging challenge....

ScribSD+: Scribble-supervised medical image segmentation based on simultaneous multi-scale knowledge distillation and class-wise contrastive regularization.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Despite that deep learning has achieved state-of-the-art performance for automatic medical image segmentation, it often requires a large amount of pixel-level manual annotations for training. Obtaining these high-quality annotations is time-consuming...

Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography.

Journal of chromatography. A
Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic...

A model for identifying potentially inappropriate medication used in older people with dementia: a machine learning study.

International journal of clinical pharmacy
BACKGROUND: Older adults with dementia often face the risk of potentially inappropriate medication (PIM) use. The quality of PIM evaluation is hindered by researchers' unfamiliarity with evaluation criteria for inappropriate drug use. While tradition...

Varroa Mite Counting Based on Hyperspectral Imaging.

Sensors (Basel, Switzerland)
Varroa mite infestation poses a severe threat to honeybee colonies globally. This study investigates the feasibility of utilizing the HS-Cam and machine learning techniques for Varroa mite counting. The methodology involves image acquisition, dimensi...

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

BMC medical imaging
OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrh...

Human manipulation strategy when changing object deformability and task properties.

Scientific reports
Robotic literature widely addresses deformable object manipulation, but few studies analyzed human manipulation accounting for different levels of deformability and task properties. We asked participants to grasp and insert rigid and deformable objec...

A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning.

Scientific reports
Cerebral aneurysms are a silent yet prevalent condition that affects a significant global population. Their development can be attributed to various factors, presentations, and treatment approaches. The importance of selecting the appropriate treatme...

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.

Nature neuroscience
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computa...