AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation

Sun, Yeheng; Ji, Yule

doi:10.1371/journal.pone.0256830

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…These operations not only save the spatial information of the input image but also realize the prediction of each pixel, so as to obtain the image segmentation result. However, when the FCN model restores the features to the original image size, there will be deviations in pixel positioning, and pixel predictions with inaccurate positions will also have a certain impact on the segmentation results [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Prostate Ultrasound Image Segmentation Based on DSU-Net

et al. 2023

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In recent years, the incidence of prostate cancer in the male population has been increasing year by year. Transrectal ultrasound (TRUS) is an important means of prostate cancer diagnosis. The accurate segmentation of the prostate in TRUS images can assist doctors in needle biopsy and surgery and is also the basis for the accurate identification of prostate cancer. Due to the asymmetric shape and blurred boundary line of the prostate in TRUS images, it is difficult to obtain accurate segmentation results with existing segmentation methods. Therefore, a prostate segmentation method called DSU-Net is proposed in this paper. This proposed method replaces the basic convolution in the U-Net model with the improved convolution combining shear transformation and deformable convolution, making the network more sensitive to border features and more suitable for prostate segmentation tasks. Experiments show that DSU-Net has higher accuracy than other existing traditional segmentation methods.

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Section: Introductionmentioning

confidence: 99%

Prostate Ultrasound Image Segmentation Based on DSU-Net

et al. 2023

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“…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][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.…”

Section: Mass Segmentation Methods Based On Cnnsmentioning

confidence: 99%

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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AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI

Zheng,

Hung,

Miao

et al. 2024

Sci Rep

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Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the model’s detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.

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AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation

Cited by 3 publications

References 45 publications

Prostate Ultrasound Image Segmentation Based on DSU-Net

Prostate Ultrasound Image Segmentation Based on DSU-Net

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

AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI

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