AIMC Topic: Humans

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Deep Learning Radiomics Nomogram Based on MRI for Differentiating between Borderline Ovarian Tumors and Stage I Ovarian Cancer: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics nomogram (DLRN) based on T2-weighted MRI to distinguish between borderline ovarian tumors (BOTs) and stage I epithelial ovarian cancer (EOC) preoperatively.

Can ChatGPT play a significant role in anatomy education? A scoping review.

Morphologie : bulletin de l'Association des anatomistes
PURPOSE: We performed a scoping review to explore if ChatGPT, an artificial intelligence platform, can play a significant role in anatomy education.

An Efficient Acute Lymphoblastic Leukemia Screen Framework Based on Multi-Modal Deep Neural Network.

International journal of laboratory hematology
BACKGROUND: Acute lymphoblastic leukemia (ALL) is a leading cause of death among pediatric malignancies. Early diagnosis of ALL is crucial for minimizing misdiagnosis, improving survival rates, and ensuring the implementation of precise treatment pla...

SSIM over MSE: A new perspective for video anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Video anomaly detection plays a crucial role in ensuring public safety. Its goal is to detect abnormal patterns contained in video frames. Most existing models distinguish the anomalies based on the Mean Squared Error (MSE), which is hard to align wi...

A meta-learning imbalanced classification framework via boundary enhancement strategy with Bayes imbalance impact index.

Neural networks : the official journal of the International Neural Network Society
For imbalanced classification problem, algorithm-level methods can effectively avoid the information loss and noise introduction of data-level methods. However, the differences in the characteristics of the datasets, such as imbalance ratio, data dim...

Fuzzy bifocal disambiguation for partial multi-label learning.

Neural networks : the official journal of the International Neural Network Society
In partial multi-label learning (PML), each instance is associated with multiple candidate labels, but only a subset is the ground-truth label. Due to the ambiguous label information, PML is more challenging than traditional multi-label learning. Con...

Interpretable machine learning models for COPD ease of breathing estimation.

Medical & biological engineering & computing
Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of...

Dynamic spectrum-driven hierarchical learning network for polyp segmentation.

Medical image analysis
Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achiev...

When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.

Medical image analysis
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the rece...

Exploring artificial intelligence literacy and the use of ChatGPT and copilot in instruction on nursing academic report writing.

Nurse education today
BACKGROUND: Nursing education increasingly emphasizes academic writing and communication, critical for delivering quality patient care and professional advancement. Rapidly emerging artificial intelligence (AI) tools such as ChatGPT and Copilot are t...