Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

CBAM-Xception: An Attention-Guided Framework for Skin Cancer Classification.

Journal of imaging informatics in medicine·2026
Same author

A dataset of exocentric images capturing hand gestures in multiplayer games.

Data in brief·2025
Same author

A novel deep neural architecture for efficient and scalable multidomain image classification.

Scientific reports·2025
Same author

Multi-Scale Attention Fusion With Depthwise Separable Convolutions for Efficient Skin Cancer Detection.

Journal of cutaneous pathology·2025
Same author

A vision-language model for multitask classification of memes.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

GATmath and GATLc: Comprehensive benchmarks for evaluating Arabic large language models.

PloS one·2025
Same journal

Radiomics-based causal machine learning for exploratory treatment-effect estimation of neoadjuvant chemotherapy cycle intensity in osteosarcoma: a proof-of-concept study.

BMC medical imaging·2026
Same journal

Gestational age-specific MRI reference values for fetal renal morphology and ADC.

BMC medical imaging·2026
Same journal

MRI findings of intrahepatic cholangiocarcinoma with sarcomatoid differentiation: a retrospective case series.

BMC medical imaging·2026
Same journal

Multimodal deep learning for papillary thyroid carcinoma diagnosis using ultrasound and cytology.

BMC medical imaging·2026
Same journal

MonoGID: geometry and illumination aware enhancement with distillation for self-supervised monocular endoscopic depth estimation.

BMC medical imaging·2026
Same journal

Application of transformer attention mechanism-based multimodal deep learning model in the diagnosis of papillary thyroid carcinoma.

BMC medical imaging·2026
查看所有相关文章

相关实验视频

Updated: Jun 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

用深度卷积神经网络进行乳房镜分类的细分.

Dip Kumar Saha1, Tuhin Hossain2, Mejdl Safran3

  • 1Department of Computer Science and Engineering, Stamford University Bangladesh, Siddeswari, Dhaka, Bangladesh.

BMC medical imaging
|December 19, 2024
PubMed
概括
此摘要是机器生成的。

一个经过修改的变压器模型在乳房影像中准确区分良性和恶性乳腺组织,达到99.96%的准确性. 这种深度学习方法有助于早期乳腺癌 (BC) 诊断和治疗规划.

关键词:
乳腺癌是什么? 乳腺癌是什么?分类 分类 分类 分类.乳房学 乳房学 乳房学萨姆·萨姆·萨姆·萨姆是什么意思分段化 分段化 分段化 分段化

更多相关视频

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.8K

相关实验视频

Last Updated: Jun 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.8K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 乳腺扫描对于早期发现乳腺癌 (BC) 是至关重要的,但区分良性和恶性质量可能是具有挑战性的.
  • 深度学习 (DL) 计算机辅助诊断 (CAD) 模型越来越多地用于BC分类.

研究的目的:

  • 为了评估一个改进的变压器模型,以区分良性和恶性乳腺组织在乳房影像.
  • 为了增强兴趣区域 (ROI) 提取,使用细分任何模型 (SAM).

主要方法:

  • 利用INbreast数据集,其中包含良性和恶性乳腺组织.
  • 修改了用于BC识别的金字塔变压器 (PTr) 架构.
  • 员工转移学习 (TL) 和SAM用于优化ROI提取.

主要成果:

  • 拟议的PTr模型实现了99.96%的准确性和99.98%的AUC对二进制分类.
  • 性能与视觉变压器 (ViT),MobileNetV3和EfficientNetB7.7进行了比较.

结论:

  • 一个经过修改的变压器模型与细分有效地分类乳腺组织从乳房图像.
  • 准确分类良性和恶性组织,支持放射科医生在治疗决策.