AIMC Topic: Algorithms

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Construction of a machine learning-based screening model for IgD myeloma.

Clinica chimica acta; international journal of clinical chemistry
OBJECTIVE: Immunoglobulin D (IgD) myeloma is a rare subtype of multiple myeloma (MM), comprising approximately 1 %-2 % of all MM cases. Owing to the diminished levels of IgD in serum, IgD MM manifests as subtle M protein spikes in routine serum elect...

HVUNet: A hybrid vision transformer-based UNet for accurate detection and localization in histopathology images.

Computers in biology and medicine
Precise identification of object of interest (OoI) in histopathology images plays a vital role in cancer diagnosis and prognosis. Despite advances in digital pathology, detecting specific cellular structures within these images remains a significant ...

Domain-incremental white blood cell classification with privacy-aware continual learning.

Scientific reports
White blood cell (WBC) classification plays a vital role in hematology for diagnosing various medical conditions. However, it faces significant challenges due to domain shifts caused by variations in sample sources (e.g., blood or bone marrow) and di...

Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images.

Scientific reports
Birth complications, particularly jaundice, are one of the leading causes of adolescent death and disease all over the globe. The main severity of these illnesses may diminish if scholars study more about their sources and progress toward effective t...

Predicting clozapine-induced adverse drug reaction biomarkers using machine learning.

Scientific reports
Clozapine is an atypical antipsychotic used for patients with treatment-resistant schizophrenia. This drug has serious adverse drug reactions (ADRs), including the risk of severe neutropenia (agranulocytosis). Patients who could benefit from clozapin...

Advanced finite segmentation model with hybrid classifier learning for high-precision brain tumor delineation in PET imaging.

Scientific reports
Brain tumor segmentation plays a crucial role in clinical diagnostics and treatment planning, yet accurate and efficient segmentation remains a significant challenge due to complex tumor structures and variations in imaging modalities. Multi-feature ...

OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes.

Scientific reports
Accurate identification of Oudemansiella raphanipes growth stages is crucial for understanding its development and optimizing cultivation. However, deep learning methods for this task remain unexplored. This paper introduces OR-FCOS, an enhanced full...

Efficacy of swarm-based neural networks in automated depression detection.

Scientific reports
As depression becomes a global pandemic, this research paper presents a comprehensive study for depression diagnosis using a custom-crafted deep learning model optimized with various swarm intelligence algorithms. Three different optimization algorit...

Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients.

Scientific reports
Following complete mesocolic excision (CME), heart failure (HF) emerges as a significant complication, exerting substantial impacts on both short-term and long-term patient prognoses. The primary objective of our investigation was to develop a machin...

Personalizing brain stimulation: continual learning for sleep spindle detection.

Journal of neural engineering
Personalized stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications....