OCASD耳镜分类与摘要数据集
OCASD耳镜分类与摘要数据集公开提供多模态数据，围绕耳镜图像分类、摘要和鼓膜穿孔相关表现相关病例、标注或临床/组学信息，适合用于分类、算法训练、外部验证、表型分析和疾病机制探索。
基本信息
资源简介
OCASD耳镜分类与摘要数据集是与耳镜图像分类、摘要和鼓膜穿孔相关表现相关的公开数据集，包含多模态数据，可用于分类、分型、诊断或预后研究。
下载信息
注册下载
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使用方式
数据集获取
git clone https://github.com/anas2908/Sumotosima.git
curl -L -o repo.zip https://github.com/anas2908/Sumotosima/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Sumotosima
(Sumariser for otoscopic image) : A novel framework and dataset for classifying and generating summaries for otoscopic images of the middle ear, with the objective of developing summaries that are both well-defined and patient-friendly, addressing the challenge of insufficient explanations from medical professionals due to their hectic schedules and limited time per patient.
Abstract
Otoscopy is a diagnostic procedure to examine the ear canal and eardrum using an otoscope. It identifies conditions like infections, foreign bodies, ear drum perforations and ear abnormalities. We propose a novel resource efficient deep learning and transformer based framework, Sumotosima (Summarizer for otoscopic images), an end-to-end pipeline for classification followed by summarization. Our framework works on combination of triplet and cross-entropy losses. Additionally, we use Knowledge Enhanced Multimodal BART whose input is fused textual and image embedding. The objective is to provide summaries that are well-suited for patients, ensuring clarity and efficiency in understanding otoscopic images. Given the lack of existing datasets, we have curated our own OCASD (Otoscopic Classification And Summary Dataset), which includes 500 images with 5 unique categories annotated with their class and summaries by Otolaryngologists. Sumotosima achieved a result of 98.03%, which is 7.00%, 3.10%, 3.01% higher than K-Nearest Neighbors, Random Forest and Support Vector Machines, respectively, in classification tasks. For summarization, Sumotosima outperformed GPT-4o and LLaVA by 88.53% and 107.57% in ROUGE scores, respectively. We have made our code and dataset publicly available
Citation
If you find our codes or paper helpful, please consider citing.
@misc{khan2024sumotosimaframeworkdatasetclassifying,
title={Sumotosima: A Framework and Dataset for Classifying and Summarizing Otoscopic Images},
author={Eram Anwarul Khan and Anas Anwarul Haq Khan},
year={2024},
eprint={2408.06755},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.06755},
}
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
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