MommiNet-v2: Mammographic multi-view mass identification networks

Yang, Zhicheng; Cao, Zhigang; Zhang, Yanbo; Tang, Yuxing; Lin, Xiaohui; Ouyang, Rushan; Wu, Mingxiang; Han, Moonsik; Xiao, Jing; Huang, Lingyun; Wu, Shibin; Chang, Peng; Ma, Jie

doi:10.1016/j.media.2021.102204

Cited by 55 publications

(46 citation statements)

References 26 publications

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“…One perspective of this work is to include multi-view detection 22 as out current model detects lesions independently of views. Unifying the detections should improve model performance.…”

Section: Discussionmentioning

confidence: 99%

Lesion detection in contrast enhanced spectral mammography

Jailin¹,

Milioni²,

Li³

et al. 2022

16th International Workshop on Breast Imaging (IWBI2022)

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Background & purpose: The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This approach was not yet developed in Contrast Enhanced Spectral Mammography (CESM) where access to large databases is complex. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases.Material & methods: A large CESM diagnostic dataset with biopsy-proven lesions was collected from various hospitals and different acquisition systems. The annotated data were split on a patient level for the training (55%), validation (15%) and test (30%) of a deep neural network with a state-of-the-art detection architecture. Free Receiver Operating Characteristic (FROC) was used to evaluate the model for the detection of 1) all lesions, 2) biopsied lesions and 3) malignant lesions. ROC curve was used to evaluate breast cancer classification. The metrics were finally compared to clinical results.Results: For the evaluation of the malignant lesion detection, at high sensitivity (Se>0.95), the false positive rate was at 0.61 per image. For the classification of malignant cases, the model reached an Area Under the Curve (AUC) in the range of clinical CESM diagnostic results. Conclusion:This CAD is the first development of a lesion detection and classification model for CESM images. Trained on a large dataset, it has the potential to be used for helping the management of biopsy decision and for helping the radiologist detecting complex lesions that could modify the clinical treatment.

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“…One perspective of this work is to include multi-view detection 22 as out current model detects lesions independently of views. Unifying the detections should improve model performance.…”

Section: Discussionmentioning

confidence: 99%

Lesion detection in contrast enhanced spectral mammography

Jailin¹,

Milioni²,

Li³

et al. 2022

16th International Workshop on Breast Imaging (IWBI2022)

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“…Furthermore, the AC block supports various input resolutions during inference; (III) The AC block consumes and produces spatial feature maps. Thus, it integrates seamlessly into architectures designed for classification [13,30,31,26,42], detection [31,23], and segmentation [34,12,40,37,45,17]. To capitalize on these three capabilities, it is vital to reduce the attention layer's computational cost.…”

Section: Attention-convolution (Ac) Blockmentioning

confidence: 99%

“…Transformers are ubiquitous in medical imaging applications. They have been used for classification [13,30,42,26,33], segmentation [34,12,40,37,45,17], and image denoising [46,25]. Yet, this recent literature leverages low resolution inputs to avoid the computational cost challenge.…”

Section: Introductionmentioning

confidence: 99%

Deep is a Luxury We Don’t Have

Taha¹,

Vu²,

Mombourquette³

et al. 2022

Lecture Notes in Computer Science

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Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling long range dependencies. Shallow transformers eliminate this problem, but they suffer from quadratic complexity. In this paper, we tackle this complexity by leveraging a linear self-attention approximation. Through this approximation, we propose an efficient vision model called HCT that stands for High resolution Convolutional Transformer. HCT brings transformers' merits to high resolution images at a significantly lower cost. We evaluate HCT using a high resolution mammography dataset. HCT is significantly superior to its CNN counterpart. Furthermore, we demonstrate HCT's fitness for medical images by evaluating its effective receptive field. Code available at https://bit.ly/3ykBhhf

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“…Therefore, a great amount of computer-aided detection (CAD) systems have been developed to improve the efficiency of mammogram interpretation. Recently, deep learning models have been used to measure the likelihood of cancer from a mammogram [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], some of which were designed to detect and classify micro-calcifications or calcified lesions [ 8 , 9 , 10 ], mass lesions [ 11 , 12 , 13 , 14 ] and even all the contained lesions [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Breast lesions found in mammograms are mainly classified into normal, benign and malignant [ 15 , 16 , 17 , 18 , 19 , 20 ] and are further classified as normal, benign calcification, benign mass, malignant calcification and malignant mass [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography

Tsai

Chou

et al. 2022

Sensors

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Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.

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MommiNet-v2: Mammographic multi-view mass identification networks

Cited by 55 publications

References 26 publications

Lesion detection in contrast enhanced spectral mammography

Lesion detection in contrast enhanced spectral mammography

Deep is a Luxury We Don’t Have

A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography

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