Weakly Supervised Deep Learning Approach to Breast MRI Assessment

Liu, Michael Z.; Swintelski, Cara; Sun, Shen-Su; Siddique, Maham; Desperito, Elise; Jambawalikar, Sachin; Ha, Richard

doi:10.1016/j.acra.2021.03.032

Cited by 21 publications

(7 citation statements)

References 19 publications

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“…Liu et al [ 30 ] used CNN to analyze and detect breast cancer on T1 DCE-MRI images from 438 patients, 131 from I-SPY clinical trials and 307 from Columbia University. Segmentation was performed through an automated process involving fuzzy C-method after seed points were manually indicated.…”

Section: Resultsmentioning

confidence: 99%

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

et al. 2023

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Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.

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Section: Resultsmentioning

confidence: 99%

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

et al. 2023

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“…Medical image datasets are often limited in size because of the difficulty in data collection and annotation [45], [46]. It is hard to train a good deep neural network for BI-RADS classification of BUS images on a small-size dataset.…”

Section: Data Augmentationmentioning

confidence: 99%

Incorporating Tumor Edge Information for Fine-Grained BI-RADS Classification of Breast Ultrasound Images

Xu,

Huang,

Huang

et al. 2024

IEEE Access

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Breast Ultrasound (BUS) imaging is an essential tool for the early detection of breast cancer. The Breast Imaging Reporting and Data System (BI-RADS) in BUS images helps standardize the interpretation and reporting process by categorizing breast tumors into multiple classes, which enables radiologists to make more accurate diagnoses and treatment plans. However, most existing classification methods distinguish only between benign and malignant categories. In addition, features extracted by classic convolutional neural networks tend to be insufficient when subdividing BUS images into fine-grained BI-RADS classes, as they typically do not consider prior knowledge in medical applications, such as foreground shape. To address the above problems, we propose a novel fine-grained BI-RADS classification approach that integrates tumor edges to provide more efficient discriminative features. Firstly, weakly supervised pseudo-label generation: we detect coarse tumor edge regions utilizing a pre-trained PiDiNet and two novel loss functions based on prior knowledge from our dataset. The detected tumor edges are subsequently used as pseudo-labels for the next step. Secondly, co-training a tumor edge detection network and a BI-RADS classification network: edge images generated by the edge detection network are used as weight masks to highlight tumor edge regions as discriminative parts for better classification results, especially for categories with high similarities. The proposed method is evaluated on a BUS image dataset of 1061 images with BI-RADS categories. Experimental results indicate that the proposed method significantly improves over the baseline model by 4.73% in terms of top-1 accuracy.INDEX TERMS Weakly supervised learning, breast ultrasound, deep learning, BI-RADS classification, edge detection.

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“…Kim et al [ 34 ] develoep a weakly-supervised DL algorithm that diagnoses breast cancer at ultrasound without image annotation. Liu et al [ 35 ] evaluated a weakly supervised deep learning approach to breast magnetic resonance imaging (MRI) assessment and showed that it is feasible to assess breast MRI images without the need for pixel-by-pixel segmentation using the weakly supervised learning method to yield a high degree of specificity in lesion classification. Lu et al [ 36 ] built a weakly supervised clustering-constrained-attention multiple-instance learning (CLAM) model for data-efficient WSI processing and learning that only requires slide-level labels.…”

Section: Related Workmentioning

confidence: 99%

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

Wang

Lee

Chang

et al. 2022

Cancers

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Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p<0.005).

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Weakly Supervised Deep Learning Approach to Breast MRI Assessment

Cited by 21 publications

References 19 publications

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

Incorporating Tumor Edge Information for Fine-Grained BI-RADS Classification of Breast Ultrasound Images

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

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