Abstract
Deep learning based neural networks have gained popularity for a variety of biomedical imaging applications. In the last few years several works have shown the use of these methods for colon cancer detection and the early results have been promising. These methods can potentially be utilized to assist doctor's and may help in identifying the number of lesions or abnormalities in a diagnosis session. From our literature survey we found out that there is a lack of publicly available labeled data. Thus, as part of this work, we have aimed at open sourcing a dataset which contains annotations of polyps and ulcers. This is the first dataset that's coming from India containing polyp and ulcer images. The dataset can be used for detection and classification tasks. We also evaluated our dataset with several popular deep learning object detection models that's trained on large publicly available datasets and found out empirically that the model trained on one dataset works well on our dataset that has data being captured in a different acquisition device.
Abstract (translated)
深度学习为基础的神经网络在众多生物医学成像应用中变得越来越受欢迎。过去几年中,几项工作已经展示了这些方法用于 colon cancer 检测的潜力,早期结果非常令人鼓舞。这些方法可以 potentially 用于协助医生,并在诊断 session 中帮助确定病变或异常情况的数量。从我们的文献调查中可以看出,缺乏公开可用的标签数据。因此,作为 this 工作的一部分,我们旨在开源包含息肉和溃疡标注的 dataset。这是来自印度的包含息肉和溃疡图像的首个 dataset。dataset 可用于检测和分类任务。我们还与几个流行的深度学习物体检测模型一起评估了我们的 dataset,这些模型在大规模公开可用数据集上训练过,并发现,在数据由不同的采集设备捕获的我们的 dataset 中,训练过的模型在包含我们的数据集上表现良好。
URL
https://arxiv.org/abs/2301.09219
