4.6 Article

SDE-YOLO: A Novel Method for Blood Cell Detection

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

CT-Based Automatic Spine Segmentation Using Patch-Based Deep Learning

Syed Furqan Qadri et al.

Summary: This study proposes a patch-based deep learning approach for automatic CT vertebral segmentation. The method extracts discriminative features from unlabeled data using a stacked sparse autoencoder and achieves accurate segmentation of CT vertebrae.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2023)

Article Oncology

Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network

Jianpeng Ao et al.

Summary: This study demonstrates the potential of a deep learning-assisted SRS platform in evaluating the tumor grade of prostate cancer, which could help simplify the diagnostic workflow and provide timely histopathology compatible with FT treatment.

CANCER RESEARCH (2023)

Article Computer Science, Artificial Intelligence

Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching

Yan Liu et al.

Summary: In this paper, an improved feature-point pair purification algorithm based on SIFT and RANSAC is proposed to address the issue of limited imaging range in endoscopy. Experimental results validate the effectiveness of the proposed algorithm.

FRONTIERS IN NEUROROBOTICS (2022)

Article Engineering, Biomedical

TE-YOLOF: Tiny and efficient YOLOF for blood cell detection

Fanxin Xu et al.

Summary: In this research, a lightweight model based on YOLOF is proposed to address the low precision of red blood cell detection. By using EfficientNet as the backbone, utilizing Depthwise Separable Convolution to reduce the number of parameters while maintaining accuracy, and employing the Mish activation function, the proposed TE-YOLOF model achieves higher precision with fewer parameters in extensive experiments.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Mathematical & Computational Biology

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Mubashir Ahmad et al.

Summary: In this paper, a patch-based deep learning method using stacked autoencoder (SAE) is proposed for liver segmentation in CT images. Unlike traditional methods, this approach learns liver features using patches and achieves satisfactory segmentation results.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2022)

Article Automation & Control Systems

Deep Temporal Model-Based Identity-Aware Hand Detection for Space Human-Robot Interaction

Jiahui Yu et al.

Summary: The article introduces a temporal detector for real-time detection and a real-time identity-awareness module for multiple hand object identification. Extensive experiments and comparisons demonstrate the superiority of the proposed model in detection and identity-awareness capacities.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Computer Science, Artificial Intelligence

Adaptive Spatiotemporal Representation Learning for Skeleton-Based Human Action Recognition

Jiahui Yu et al.

Summary: This article introduces an adaptive skeleton-based neural network to learn optimal spatiotemporal representation automatically through a data-driven manner. The proposed model can accurately understand long-term or complex actions and achieves state-of-the-art performance on multiple benchmarks.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Spatial Cognition-Driven Deep Learning for Car Detection in Unmanned Aerial Vehicle Imagery

Jiahui Yu et al.

Summary: This article proposes a new one-stage detector (SF-SSD) for small object detection in UAV image detection, with a new spatial cognition algorithm. The detector achieves high detection accuracy by enhancing the representation of shallow features. Experimental results demonstrate the high performance of the proposed method on different datasets.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Focal and efficient IOU loss for accurate bounding box regression

Yi-Fan Zhang et al.

Summary: This paper focuses on the crucial step of bounding box regression in object detection and proposes two improvements, named EIOU loss and Focal-EIOU loss, to address the issues with previous loss functions. Experimental results demonstrate notable superiority in convergence speed and localization accuracy compared to other methods.

NEUROCOMPUTING (2022)

Article Crystallography

Fire smoke detection combined with detailed features and hybrid attention mechanism

Wang Rui-qing et al.

Summary: An improved YOLOv4 algorithm is proposed in this study to address the problem of weakening detailed features and losing low-level features in smoke images. By combining detail feature fusion and attention mechanism, the algorithm achieves robust feature expression. Experimental results show significant improvements in average precision, precision, and recall rate compared to the YOLOv4 algorithm, while maintaining high detection speed. The proposed algorithm effectively extracts overall smoke target features and is suitable for smoke detection in complex backgrounds.

CHINESE JOURNAL OF LIQUID CRYSTALS AND DISPLAYS (2022)

Article Multidisciplinary Sciences

Deep learning-based predictive identification of neural stem cell differentiation

Yanjing Zhu et al.

Summary: The differentiation of neural stem cells into neurons is crucial for potential cell-based therapeutic strategies for CNS diseases. Using deep learning, researchers have developed a reliable model for predicting NSCs fate, demonstrating high precision in identifying differentiated cell types early in culture and applicability across various inducers.

NATURE COMMUNICATIONS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

ISE-YOLO: Improved Squeeze-and-Excitation Attention Module based YOLO for Blood Cells Detection

Cong Liu et al.

Summary: The accurate detection of human peripheral blood cells is crucial for diagnosing blood-related diseases. Traditional clinical detection methods are easily influenced by subjective factors, leading to errors. The introduction of visual attention mechanism into the deep learning detection model has significantly improved the performance, outperforming other advanced methods.

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) (2021)

Article Computer Science, Hardware & Architecture

AmoebaNet: An SDN-enabled network service for big data science

S. A. R. Shah et al.

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS (2018)

Article Computer Science, Artificial Intelligence

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Xception: Deep Learning with Depthwise Separable Convolutions

Francois Chollet

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Biochemical Research Methods

Accurate measurement of peripheral blood mononuclear cell concentration using image cytometry to eliminate RBC-induced counting error

Leo Li-Ying Chan et al.

JOURNAL OF IMMUNOLOGICAL METHODS (2013)