AIMC Topic: Algorithms

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A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Computers in biology and medicine
Multimodal data, while being information-rich, contains complementary as well as redundant information. Depending on the target problem some modalities are more informative and thus relevant for decision-making. Identifying the optimal subset of moda...

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Mathematical biosciences and engineering : MBE
Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis re...

Development of a short form of the Geriatric Depression Scale-30 based on item response theory and the RiskSLIM algorithm.

General hospital psychiatry
Recently, methods of quickly and accurately screening for geriatric depression have attracted substantial attention. Short forms of the 30-item Geriatric Depression Scale have been developed based on classical test theory, such as the GDS-4, GDS-5, a...

Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models.

Sensors (Basel, Switzerland)
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over tradition...

Prior-FOVNet: A Multimodal Deep Learning Framework for Megavoltage Computed Tomography Truncation Artifact Correction and Field-of-View Extension.

Sensors (Basel, Switzerland)
Megavoltage computed tomography (MVCT) plays a crucial role in patient positioning and dose reconstruction during tomotherapy. However, due to the limited scan field of view (sFOV), the entire cross-section of certain patients may not be fully covere...

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

BMC cancer
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) exhibits a propensity for early recurrence following liver resection, resulting in a bleak prognosis. At present, majority of the predictive models for the early postoperative recurrence of HCC rely...

Learnable color space conversion and fusion for stain normalization in pathology images.

Medical image analysis
Variations in hue and contrast are common in H&E-stained pathology images due to differences in slide preparation across various institutions. Such stain variations, while not affecting pathologists much in diagnosing the biopsy, pose significant cha...

On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach.

Neural networks : the official journal of the International Neural Network Society
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining appr...

Heterophilous distribution propagation for Graph Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar be...

Promises and perils of using Transformer-based models for SE research.

Neural networks : the official journal of the International Neural Network Society
Many Transformer-based pre-trained models for code have been developed and applied to code-related tasks. In this paper, we analyze 519 papers published on this topic during 2017-2023, examine the suitability of model architectures for different task...