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Breast cancer detection through attention based feature integration... |
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by Sharada Gupta, Murundi N. Eshwarappa |
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Breast cancer is detected by screening mammography |
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wherein X-rays are used to produce images of the breast. |
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Mammograms for screening can detect breast cancer early. |
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This research focuses on the challenges of using multi- |
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view mammography to diagnose breast cancer. By examining |
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numerous perspectives of an image, an attention-based |
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feature-integration mechanism (AFIM) model that |
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concentrates on local abnormal areas associated with |
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cancer and displays the essential features considered for |
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evaluation, analyzing cross-view data. This is segmented |
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into two views the bi-lateral attention module (BAM) |
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module integrates the left and right activation maps for |
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a similar projection is used to create a spatial |
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attention map that highlights the impact of asymmetries. |
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Here the module's focus is on data gathering through |
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medio-lateral oblique (MLO) and bilateral craniocaudal |
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(CC) for each breast to develop an attention module. The |
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proposed AFIM model generates using spatial attention |
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maps obtained from the identical image through other |
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breasts to identify bilaterally uneven areas and class |
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activation map (CAM) generated from two similar breast |
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images to emphasize the feature channels connected to a |
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single lesion in a breast. AFIM model may easily be |
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included in ResNet-style architectures to develop multi- |
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view classification models. |
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Date Published: 2024-12-02 08:53:40 |
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Identifier: 43-23641 |
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Item Size: 9756051 |
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Language: eng |
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Media Type: texts |
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# Topics |
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Attention-based feature integration m... |
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