Background and SOTA Results

Here we highlight a few state-of-the-art results for breast ultrasound classification.

Classification models are typically trained in two different ways: - Strongly-supervised approach each individual image is labeled as malignant or benign.
- Weakly-supervised approach each exam, consisting of multiple images, is labeled as malignant or benign.

The strongly-supervised approach requires time consuming and expensive annotation by radiologists who must review each individual image after acquiring biopsy results. The weakly-supervised approach requires only that a breast exam has corresponding biopsy results or that a sufficient amount of time has passed so that the breast may be assume to have no malignancy.

State-of-the-art results for strongly-supervised classification.

In a 2023 comparative study Ferreira et al. (2023) combined two small public datasets (810 total images) to show that EfficientNet can be used to attain Accuracy = 92.59% and AUC = 93.53% on their validation set.

In the 2022 article “Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study” Gu et al. (2022) collected and annotated 14,043 ultrasound images and trained a VGG19 model to achieve, on a separate test set, Accuracy = 86.4%, AUC = 91.3%, Sensitivity = 88.84%, and Specificity = 83.77%.

State-of-the-art results for weakly-supervised collection.

In the 2021 article “Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams,” Shen et al. (2021) collected 288,767 exams consisting of 5,442,907 images (about 10% of the exams were associated with biopsies). They trained a multiple instance (weakly-supervised) model using a Resnet18 backbone and only exam-level labels to achieve an AUC of 97.6% on their test set (AUC = 94.0% on biopsied population). Moreover, at 90.1% sensitivity, their model demonstrated 85.6% specificity.

References

Ferreira, Margarida R., Helena R. Torres, Bruno Oliveira, Augusto R. V. F. De Araújo, Pedro Morais, Paulo Novais, and João L. Vilaça. 2023. “Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study.” In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 1–4. Sydney, Australia: IEEE. https://doi.org/10.1109/EMBC40787.2023.10340293.
Gu, Yang, Wen Xu, Bin Lin, Xing An, Jiawei Tian, Haitao Ran, Weidong Ren, et al. 2022. “Deep Learning Based on Ultrasound Images Assists Breast Lesion Diagnosis in China: A Multicenter Diagnostic Study.” Insights into Imaging 13 (1): 124. https://doi.org/10.1186/s13244-022-01259-8.
Shen, Yiqiu, Farah E. Shamout, Jamie R. Oliver, Jan Witowski, Kawshik Kannan, Jungkyu Park, Nan Wu, et al. 2021. “Artificial Intelligence System Reduces False-Positive Findings in the Interpretation of Breast Ultrasound Exams.” Nature Communications 12 (1): 5645. https://doi.org/10.1038/s41467-021-26023-2.