Compared to general computer vision tasks, medical image analysis faces a shortage of large-scale and well-annotated datasets, primarily due to the much higher cost of annotation. In 2018, Yan et al. proposed a brand new CT scans dataset named “DeepLesion (Yan et al. 2018a)” which contai...
Moreover, data scarcity remains a significant challenge; however, this can be overcome through data generation or transfer learning. For instance, the dataset with image pairs could be supplemented through random translation, rotation, brightness, and contrast enhancement using retinal images from other ...
layers. MBSNet was tested on five datasets ISIC2018, Kvasir, BUSI, COVID-19, and LGG. The combined results show that MBSNet is lighter, faster, and more accurate. Specifically, for a320×320input, MBSNet’s FLOPs is 10.68G, with an F1-Score of85.29%on the Kvasir test dataset, well ...
The objective of the human annotation study was to quantitatively evaluate how MedSAM can reduce the annotation time cost. Specifically, we used the recent adrenocortical carcinoma CT dataset34,42,43, where the segmentation target, adrenal tumor, was neither part of the training nor of the existin...
SIIM-ACRhttps://www.kaggle.com/datasets/jesperdramsch/siim-acr-pneumothorax-segmentation-dataOpen Access The split of each dataset can be found inhttps://huggingface.co/datasets/chaoyi-wu/RadFM_data_csvyou just need to download the image part from each datasets. ...
The combined dataset is grouped into three categories: To investigate the capabilities of machine learning algorithms for image tampering detection, eight different machine learning algorithms, which include three conventional machine learning methods, SVM, Random Forest, Decision Tree, and five deep learni...
The classification performance of few-shot baselines on each dataset is shown in Table2. More data can often provide better support for distinguishing the representations of testing data, but it comes with a higher data demand and more extensive computation cost. The classification performance on fiv...
they found that pre-training with data similar to downstream tasks is better than directly using the pre-training weights of ImageNet. Secondly, the smaller the dataset, the greater the pre-training effect. The other two findings are that the more pre-trained images, the larger the size, whi...
Post load- ing the respective model, learning weights were modified to the suitability of the present dataset. The details of the dataset used, details of model selec- tion and process of model architecture are mentioned in the following sections. 3.1 Description of dataset This study has...
3, we first train the context encoder VT , VXray and the cross- attention sub-layer weights of the text and X-ray diffusers on the text-Xray paired dataset. Then we freeze the train- able parameter of the text diffuser and train the context en...