A Deep Feature Fusion Methodology for Breast Cancer Diagnosis Demonstrated on Three Imaging Modality Datasets

By Natalia Antropova, Benjamin Q. Huynh, Maryellen Lissak Giger. Medical Physics (Lancaster), 2017

358 citations recorded by the source corpus.

Background: Deep learning methods for radiomics/computer‐aided diagnosis (CADx) are often prohibited by small datasets, long computation time, and the need for extensive image preprocessing. Aims: We aim to develop a breast CADx methodology that addresses the aforementioned issues by exploiting the efficiency of pre‐trained convolutional neural networks (CNNs) and using pre‐existing handcrafted CADx features. Materials & Methods: We present a methodology that extracts and pools low‐ to mid‐level features using a pretrained CNN and fuses them with handcrafted radiomic features computed using conventional CADx methods. Our methodology is tested on three different clinical imaging modalities (dynamic contrast enhanced‐MRI [690 cases], full‐field digital mammography [245 cases], and ultrasound [1125 cases]). Results: From ROC analysis, our fusion‐based method demonstrates, on all three imaging modalities, statistically significant improvements in terms of AUC as compared to previous breast cancer CADx methods in the task of distinguishing between malignant and benign lesions. (DCE‐MRI [AUC = 0.89 (se = 0.01)], FFDM [AUC = 0.86 (se = 0.01)], and ultrasound [AUC = 0.90 (se = 0.01)]). Discussion/Conclusion: We proposed a novel breast CADx methodology that can be used to more effectively characterize breast lesions in comparison to existing methods. Furthermore, our proposed methodology is computationally efficient and circumvents the need for image preprocessing.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/907ac4d30e4fc9fc5059b35647b3a853ad5085ea

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Citations in the corpus

Deep Learning in Medical Ultrasound Analysis: A Review (2019, methodology): “In the work of Antropova et al. [91], a method that fuses the extracted and pooled low-and mid-level features using a pre-trained CNN with hand-designed features using conventional CADx methods was applied to three clinical imaging modality datasets, and demonstrated signiﬁcant performance…”
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “Several previous studies utilized radiomics for the prediction of breast cancer subtype or the status of ER, PR, Ki67 and HER2 with US [46], mammography [118], PET/CT [119, 120], and MRI [121, 122].”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “196,197 Figure 4 shows one possible method for combining CNN-extracted and conventional radiomics features.”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “Off-the-shelf features have performed well in a variety of domains,(23) and this technique has been successfully applied to medical imaging.(29,30) In Antropova et al,(29) the authors combined the deep off-the-shelf features extracted from a pretrained deep CNN network with hand-crafted features for”