Improving lesion detection algorithm in Digital Breast Tomosynthesis leveraging ensemble cross-validation models with multi-depth levels
In person: 21 February 2022 • 8:40 AM - 9:00 AM PST
We report an improved algorithm for detecting biopsy-proven lesions on digital breast tomosynthesis using a small training set from our DBTex challenge participation. To tackle small samples, all top-ranked algorithms (1st–3rd) used large inhouse datasets. We hypothesized using false positive findings (FPs) by detection algorithms from non-biopsied samples in the training set as an alternative to using inhouse datasets. We used FPs for augmentation and proposed an ensemble approach to fuse multiple detection models using cross-validation and changing the model-depth. Using a challenge-validation set, we achieved a mean sensitivity of 0.84, close to one of the top algorithms.
Univ. of Pittsburgh (United States)