AIMC Topic: Humans

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Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

International journal of laboratory hematology
INTRODUCTION: Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, alth...

Improving span-based Aspect Sentiment Triplet Extraction with part-of-speech filtering and contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible spans of...

Conquering class imbalances in deep learning-based segmentation of dental radiographs with different loss functions.

Journal of dentistry
OBJECTIVE: The imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic imag...

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

Japanese journal of radiology
PURPOSE: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions.

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

Magnetic resonance in medicine
PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.

Use of Artificial Intelligence Software to Detect Intracranial Aneurysms: A Comprehensive Stroke Center Experience.

World neurosurgery
OBJECTIVE: To evaluate variability in aneurysm detection and the potential of artificial intelligence (AI) software as a screening tool by comparing conventional computed tomography angiography (CTA) images (standard care) with AI software.

Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation.

Medical image analysis
The success of deep learning on image classification and recognition tasks has led to new applications in diverse contexts, including the field of medical imaging. However, two properties of deep neural networks (DNNs) may limit their future use in m...

Vein pattern visualisation for biometric identification with cGAN on a New Zealand dataset.

Forensic science international
Forensic identification using vein patterns in standard colour images presents significant challenges due to their low visibility. Recent efforts have employed various computational techniques, including artificial neural networks and optical vein di...

Assessing the Suitability of Artificial Intelligence-Based Chatbots as Counseling Agents for Patients with Brain Tumor: A Comprehensive Survey Analysis.

World neurosurgery
OBJECTIVE: The internet, particularly social media, has become a popular resource for learning about health and investigating one's own health conditions. The development of artificial intelligence (AI) chatbots has been fueled by the increasing avai...

N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites.

Methods (San Diego, Calif.)
Studies have shown that protein glycosylation in cells reflects the real-time dynamics of biological processes, and the occurrence and development of many diseases are closely related to protein glycosylation. Abnormal protein glycosylation can be us...