First, we examined model tolerance for noisy data by incrementally increasing error in the labels within the training data.
Model performance on virtual CT and CXR images was comparable to overall results on clinical data.
However, performance dropped to an AUC of 0. 65 and 0. 69 when evaluated on clinical and our simulated CVIT-COVID dataset.
This demonstrates that quality data is the key to improving the model's performance.
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD).
Pre-trained models outperformed random initialization across all diseases.
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials & Methods: This retrospective study included a total of 12, 092 patients (mean age 57 +- 18; 6, 172 women) for model development and testing (from 2012-2017).
We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 and PH2.
The contribution of this paper is to present and compare two different approaches to skin lesion segmentation.
The outcome of the research indicates that for the IBSR18 data-set, pre-processing and proper tuning of hyper-parameters for NeuroNet model have improvement in DSC for the brain tissue segmentation.
For analysis cardiac functionality, extracting information from the Left ventricular (LV) is already a broad field of Medical Imaging.