简介 Intro
人喉表皮样癌细胞图像分类模型是专为细胞图像分类任务设计的深度学习模型，采用了深度卷积神经网络技术。该模型的训练数据来自 HEp-2 细胞图像数据集，该数据集源于 2014 年国际模式识别大会的细胞图像分类竞赛。数据集包括图像分为训练集 (8,701 张图像)、验证集 (2,175 张图像)和测试集 (2,720 张图像)。此外，还提供了一个 .csv 文件，包含两列数据：第一列是图像 ID，与三个数据集中的图像名称相匹配；第二列是细胞图像的类别。该模型借鉴了 AlexNet 的经典结构，以深度卷积神经网络为基础，包括卷积层、池化层和全连接层等组件，具有强大的图像特征学习能力。其主要训练目标是获取 HEp-2 细胞图像分类任务的判别性特征，以提高在验证集和测试集上的分类性能。该模型的训练旨在高效捕获 HEp-2 细胞图像中的关键信息，以便准确地对图像进行分类。
The HEp-2 cell image classification model is a deep learning model designed specifically for the cell image classification task, using deep convolutional neural network techniques. The training data for the model comes from the HEp-2 cell image dataset, which originated from the cell image classification competition at the 2014 International Pattern Recognition Conference. The dataset consists of images categorized into a training set (8,701 images), a validation set (2,175 images), and a test set (2,720 images). In addition, a .csv file is provided containing two columns of data: the first column is the image ID, which matches the name of the image in the three datasets; the second column is the category of the cell image. The model draws on the classical structure of AlexNet and is based on a deep convolutional neural network, including components such as convolutional, pooling, and fully connected layers, with powerful image feature learning capabilities. Its main training goal is to acquire discriminative features for the HEp-2 cell image classification task in order to improve the classification performance on both validation and test sets. The model is trained to efficiently capture critical information in HEp-2 cell images in order to accurately classify the images.
在线体验 Demo
https://www.modelscope.cn/studios/MuGemSt/HEp2
维护 Maintenance
git clone git@hf.co:MuGemSt/HEp2
cd HEp2
使用 Usage
from modelscope import snapshot_download
model_dir = snapshot_download("MuGemSt/HEp2")
数据集 Dataset
https://www.modelscope.cn/datasets/MuGemSt/HEp2
训练曲线 Training curve
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镜像 Mirror
https://www.modelscope.cn/models/MuGemSt/HEp2
参考引用 Reference
[1] https://github.com/MuGemSt/Medical_Image_Computing/tree/hep2