Improving lesion detection algorithm in digital breast tomosynthesis leveraging ensemble cross-validation models with multi-depth levels

We report an improved algorithm for detecting biopsy-proven breast lesions on digital breast tomosynthesis (DBT) where the given positive samples in the training set were limited. Instead of using a large scale inhouse dataset, our original algorithm used false positive findings (FPs) from non-biopsied (actionable) images to tackle the problem of a limited number of trainable samples. In this study, we further improved our algorithm by fusing multiple weak lesion detection models by using an ensemble approach. We used cross-validation (CV) to develop multiple lesion detection models. We first constructed baseline detection algorithms by varying the depth levels (medium and large) of the convolutional layers in the YOLOv5 algorithm using biopsied samples. We detected actionable FPs in non-biopsied images using a medium baseline model. We fine-tuned the baseline algorithms using the identified actionable FPs and the biopsied samples. For lesion detection, we processed the DBT volume slice-by-slice, then combined the estimated lesion of each slice along the depth of the DBT volume using volumetric morphological closing. Using 5-fold CV, we developed different multi-depth detection models for each depth level. Finally, we developed an ensemble algorithm by combining CV models with different depth levels. Our new algorithm achieved a mean sensitivity of 0.84 per DBT volume in the independent validation set from the DBTex challenge set, close to one of the top performing algorithms utilizing large inhouse data. These results show that our ensemble approach on different CV models is useful for improving the performance of the lesion detection algorithms.