YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

Su, Yongye; Liu, Qian; Xie, Wenhui; Hu, Pingzhao

doi:10.1016/j.cmpb.2022.106903

Cited by 45 publications

(17 citation statements)

References 32 publications

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“…21 To cope with the imbalance problem, most previous studies have opted for (1) segmenting masses within extracted ROIs 6,8,14,[22][23][24] or (2) relying on an additional mass detection stage. 25,26 Zhu et al 15 proposed an adversarial FCN-CRF network for mass segmentation from ROIs. The ROI was achieved by a tight bounding box around the mass region.…”

Section: Mass Segmentation Methods Based On Cnnsmentioning

confidence: 99%

“…All these transformer-based methods are seldom designed for breast mass segmentation. Only Su et al 26 proposed a two-stage framework that employed the YOLO and MedT models to first identify breast masses in mammograms and then produce segmentation masks. The model of each stage had to be trained separately.…”

Section: Transformer For Image Segmentationmentioning

confidence: 99%

“…Deep neural networks tend to be overwhelmed by the large class and ignore the small one 21 . To cope with the imbalance problem, most previous studies have opted for (1) segmenting masses within extracted ROIs 6,8,14,22–24 or (2) relying on an additional mass detection stage 25,26 . Zhu et al 15 .…”

Section: Related Workmentioning

confidence: 99%

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TrEnD: A transformer‐based encoder‐decoder model with adaptive patch embedding for mass segmentation in mammograms

Liu

et al. 2023

Medical Physics

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Background: Breast cancer is one of the most prevalent malignancies diagnosed in women. Mammogram inspection in the search and delineation of breast tumors is an essential prerequisite for a reliable diagnosis. However, analyzing mammograms by radiologists is time-consuming and prone to errors. Therefore, the development of computer-aided diagnostic (CAD) systems to automate the mass segmentation procedure is greatly expected. Purpose: Accurate breast mass segmentation in mammograms remains challenging in CAD systems due to the low contrast, various shapes, and fuzzy boundaries of masses. In this paper, we propose a fully automatic and effective mass segmentation model based on deep learning for improving segmentation performance. Methods:We propose an effective transformer-based encoder-decoder model (TrEnD). Firstly, we introduce a lightweight method for adaptive patch embedding (APE) of the transformer, which utilizes superpixels to adaptively adjust the size and position of each patch. Secondly, we introduce a hierarchical transformer-encoder and attention-gated-decoder structure, which is beneficial for progressively suppressing interference feature activations in irrelevant background areas. Thirdly, a dual-branch design is employed to extract and fuse globally coarse and locally fine features in parallel, which could capture the global contextual information and ensure the relevance and integrity of local information. The model is evaluated on two public datasets CBIS-DDSM and INbreast. To further demonstrate the robustness of TrEnD, different cropping strategies are applied to these datasets, termed tight, loose, maximal, and mixframe.Finally,ablation analysis is performed to assess the individual contribution of each module to the model performance. Results:The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 92.20% and 85.81% on the mix-frame CBIS-DDSM, while 91.83% and 85.29% for the mix-frame INbreast, respectively. The segmentation performance outperforms the current state-of -the-art approaches. By adding the APE and attention-gated module, the Dice and IoU have improved by 6.54% and 10.07%. Conclusion: According to extensive qualitative and quantitative assessments, the proposed network is effective for automatic breast mass segmentation, and has adequate potential to offer technical assistance for subsequent clinical diagnoses.

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Section: Mass Segmentation Methods Based On Cnnsmentioning

confidence: 99%

Section: Transformer For Image Segmentationmentioning

confidence: 99%

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TrEnD: A transformer‐based encoder‐decoder model with adaptive patch embedding for mass segmentation in mammograms

Liu

et al. 2023

Medical Physics

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“…Multi-scale attention-based network MSANet was created by Xu et al (2021) for the categorization of mammograms for the DDSM dataset. Su et al (2022) introduced the YOLO-LOGO segmentation model, which combines the YOLO and LOGO architectures with a deep learning technique based on transformers for the identification and segmentation of breast masses for DDSM-INbreast datasets.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Das

Das²,

Neog³

et al. 2023

Front. Genet.

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Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2.Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively.Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.

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“…Convolutional neural networks (CNN) [ 25 ], recurrent neural networks (RNN) [ 26 ], generative adversarial networks (GAN) [ 27 ] and transformer neural networks [ 28 ]. Figure 1 depicts deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs

Kotei¹,

Ramkumar²

2022

Healthcare

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Tuberculosis (TB) is an infectious disease affecting humans’ lungs and is currently ranked the 13th leading cause of death globally. Due to advancements in technology and the availability of medical datasets, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative for early TB screening. We propose an automatic TB detection system using advanced deep learning (DL) models. A substantial part of a CXR image is dark, with no relevant information for diagnosis and potentially confusing DL models. In this work, the U-Net model extracts the region of interest from CXRs and the segmented images are fed to the DL models for feature extraction. Eight different convolutional neural networks (CNN) models are employed in our experiments, and their classification performance is compared based on three publicly available CXR datasets. The U-Net model achieves segmentation accuracy of 98.58%, intersection over union (IoU) of 93.10, and a Dice coefficient score of 96.50. Our proposed stacked ensemble algorithm performed better by achieving accuracy, sensitivity, and specificity values of 98.38%, 98.89%, and 98.70%, respectively. Experimental results confirm that segmented lung CXR images with ensemble learning produce a better result than un-segmented lung CXR images.

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YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

Cited by 45 publications

References 32 publications

TrEnD: A transformer‐based encoder‐decoder model with adaptive patch embedding for mass segmentation in mammograms

TrEnD: A transformer‐based encoder‐decoder model with adaptive patch embedding for mass segmentation in mammograms

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs

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