A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis

Swiecicki, Albert; Konz, Nicholas; Buda, Mateusz; Mazurowski, Maciej A.

doi:10.1038/s41598-021-89626-1

Cited by 17 publications

(10 citation statements)

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“…Generative adversarial network (GAN) is a type of neural computational network model for two networks training simultaneously 16 . The final GAN-based anomaly model could capture abnormal features from new images based on the trained normal images 17 , and several studies validated its feasibility 18 , 19 . Compared to BCCT.core software, the GAN-based approach for detecting anomalies from computed tomography (CT) images did not cause additional discomfort for patients.…”

Section: Introductionmentioning

confidence: 95%

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

et al. 2022

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The aim of this study is to evaluate cosmetic outcomes of the reconstructed breast in breast cancer patients, using anomaly score (AS) detected by generative adversarial network (GAN) deep learning algorithm. A total of 251 normal breast images from patients who underwent breast-conserving surgery were used for training anomaly GAN network. GAN-based anomaly detection was used to calculate abnormalities as an AS, followed by standardization by using z-score. Then, we reviewed 61 breast cancer patients who underwent mastectomy followed by reconstruction with autologous tissue or tissue expander. All patients were treated with adjuvant radiation therapy (RT) after reconstruction and computed tomography (CT) was performed at three-time points with a regular follow-up; before RT (Pre-RT), one year after RT (Post-1Y), and two years after RT (Post-2Y). Compared to Pre-RT, Post-1Y and Post-2Y demonstrated higher AS, indicating more abnormal cosmetic outcomes (Pre-RT vs. Post-1Y, P = 0.015 and Pre-RT vs. Post-2Y, P = 0.011). Pre-RT AS was higher in patients having major breast complications (P = 0.016). Patients with autologous reconstruction showed lower AS than those with tissue expander both at Pre-RT (2.00 vs. 4.19, P = 0.008) and Post-2Y (2.89 vs. 5.00, P = 0.010). Linear mixed effect model revealed that days after baseline were associated with increased AS (P = 0.007). Also, tissue expander was associated with steeper rise of AS, compared to autologous tissue (P = 0.015). Fractionation regimen was not associated with the change of AS (P = 0.389). AS detected by deep learning might be feasible in predicting cosmetic outcomes of RT-treated patients with breast reconstruction. AS should be validated in prospective studies.

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Section: Introductionmentioning

confidence: 95%

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

et al. 2022

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“…The most commonly applied metrics include structural similarity index measure (SSIM) 10 between generated image and reference image [148], mean squared error (MSE) 11 and peak signal-to-noise ratio (PSNR) 12 . In a recent example that followed this framework of evaluation, synthetic brain MRI with tumours generated by edge-aware EA-GAN [149] was assessed using three such metrics: PSNR, SSIM, and normalised mean squared error (NMSE).…”

Section: Synthetic Data Assessmentmentioning

confidence: 99%

“…In regard to imaging modalities, we analyse in Figure 2(b) how much research attention each modality has received in terms of the number of corresponding publications. By far, MRI and CT are the most dominant modalities with 57, and 54 publications, respectively, followed by MMG (13), dermoscopy (12) and PET (6). The wide spread between MRI and CT and less investigated domains such as endoscopy (3), ultrasound (3), and tomosynthesis (0) is to be critically examined.…”

Section: Privatementioning

confidence: 99%

“…Figure 2(a) displays the distribution of GAN publications across cancer imaging challenge categories that correspond to the subsections of Section 4. While the sections 4.4 detection and diagnosis (54) and 4.4 data annotation and segmentation (44), and 4.1 data scarcity and usability (35) have received much research attention, the sections 4.5 treatment and monitoring (18) and 4.2 data access and privacy (12) contain substantially less GAN-related publications. This spread can be anticipated considering that classification and segmentation are popular computer vision problems and common objectives in publicly available medical imaging benchmark datasets.…”

Section: Privatementioning

confidence: 99%

“…Medical imaging data evaluation is time demanding and therefore costly in nature. In addition, volumes of new technologies (e.g., digital breast tomosynthesis [12]) become available and studies generally show an extensive increase in analysable imaging volumes [13]. Also, the diagnostic quality in radiology varies and is very much dependent on the personal experience, skills and invested time of the data examiner [10,14,15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

Osuala¹,

Kushibar²,

Garrucho³

et al. 2021

Preprint

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Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include high inter-observer variability, difficulty of small-sized lesion detection, nodule interpretation and malignancy determination, inter-and intra-tumour heterogeneity, class imbalance, segmentation inaccuracies, and treatment effect uncertainty. The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis. In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data annotation and quantification, as well as cancer detection, tumour profiling and treatment planning. We provide a critical appraisal of the existing literature of GANs applied to cancer imagery, together with suggestions on future research directions to address these challenges. We analyse and discuss 163 papers that apply adversarial training techniques in the context of cancer imaging and elaborate their methodologies, advantages and limitations. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on GANs in the artificial intelligence community.

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A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis

Konz

Buda

et al. 2023

JAMA Netw Open

Self Cite

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ImportanceAn accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide.ObjectivesTo make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods.Design, Setting, and ParticipantsThis diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22 032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the full data set, took place from May to July 2021.Main Outcomes and MeasuresThe overall performance was evaluated by mean sensitivity for biopsied lesions using only DBT volumes with biopsied lesions; ties were broken by including all DBT volumes.ResultsA total of 8 teams participated in the challenge. The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). When the results were aggregated, the mean sensitivity for all submitted algorithms was 0.879; for only those who participated in phase 2, it was 0.926.Conclusions and RelevanceIn this diagnostic study, an international competition produced algorithms with high sensitivity for using AI to detect lesions on DBT images. A standardized performance benchmark for the detection task using publicly available clinical imaging data was released, with detailed descriptions and analyses of submitted algorithms accompanied by a public release of their predictions and code for selected methods. These resources will serve as a foundation for future research on computer-assisted diagnosis methods for DBT, significantly lowering the barrier of entry for new researchers.

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A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis

Cited by 17 publications

References 20 publications

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis

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