AIMC Topic: Algorithms

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Whither bias goes, I will go: An integrative, systematic review of algorithmic bias mitigation.

The Journal of applied psychology
Machine learning (ML) models are increasingly used for personnel assessment and selection (e.g., resume screeners, automatically scored interviews). However, concerns have been raised throughout society that ML assessments may be biased and perpetuat...

DICCR: Double-gated intervention and confounder causal reasoning for vision-language navigation.

Neural networks : the official journal of the International Neural Network Society
Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text ins...

Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.

Medical image analysis
Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on hi...

Multi-scale multi-object semi-supervised consistency learning for ultrasound image segmentation.

Neural networks : the official journal of the International Neural Network Society
Manual annotation of ultrasound images relies on expert knowledge and requires significant time and financial resources. Semi-supervised learning (SSL) exploits large amounts of unlabeled data to improve model performance under limited labeled data. ...

MPIC: Exploring alternative approach to standard convolution in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explore...

DFedGFM: Pursuing global consistency for Decentralized Federated Learning via global flatness and global momentum.

Neural networks : the official journal of the International Neural Network Society
To tackle high communication costs and privacy issues in Centralized Federated Learning (CFL), Decentralized Federated Learning (DFL) is an alternative. However, a significant discrepancy exists between local updates and the expected global update, k...

Fast ramp fraction loss SVM classifier with low computational complexity for pattern classification.

Neural networks : the official journal of the International Neural Network Society
The support vector machine (SVM) is a powerful tool for pattern classification thanks to its outstanding efficiency. However, when encountering extensive classification tasks, the considerable computational complexity may present a substantial barrie...

A novel lightweight deep learning based approaches for the automatic diagnosis of gastrointestinal disease using image processing and knowledge distillation techniques.

Computer methods and programs in biomedicine
BACKGROUND: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational deman...

Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay.

Environmental and molecular mutagenesis
Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode o...

Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.

Sensors (Basel, Switzerland)
Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of...