This data set can be acquired simultaneously with a conventional digital mammogram. In 2011, tomosynthesis was approved by the US Food and Drug Administration (FDA) to be used in combination with standard digital mammography for breast cancer screening.6 This combined mode (digital mammography + ...
of synthetic image patches at a high resolution of 256 × 256 pixels. The resulting ciGAN model was then applied to GAN-based augmentation, which improved mammogram patch-based classification by 0.014 AUC over the baseline model and 0.009 AUC over traditional augmentation techniques66. This stu...
Privacy concerns around sharing personally identifiable information are a major barrier to data sharing in medical research. In many cases, researchers have no interest in a particular individual’s information but rather aim to derive insights at the le
The major limitation of FFDM is its lower sensitivity in the patients with dense breasts and the fact that false-positive findings are more often observed on the first mammogram because the comparison of the previous mammogram has a crucial role in diagnosis. ABUS mainly detects benign lesions and...
The aim of this proposal is to evaluate different methods of transferring the data recorded in a mammogram to displayed intensities, in order to increase the sensitivity of mammography and facilitate earlier identification of breast cancer. The proposed research has three parts: first, we will provid...
and Mertelmeier, T. Assessing radiologist performance and microcalcifications visualization using combined 3D ro- tating mammogram (RM) and digital breast tomosynth- esis (DBT). Breast Imaging Lect. Notes Comput. Sci. Vol. 8539, 142-149 (2014)....
Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but
Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted
For breast imaging, AUC’s ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an ...