This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnificati...
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This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnification models. The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. Postprocessing techniques, including region-growing and fast-marching level set algorithms, refine whole-slide image (WSI) prediction and postprocessing quality. Performance was evaluated via metrics such as precision, recall, the F1 score, accuracy, and specificity across 50 trials with perturbed conditions. The framework achieved a top accuracy of 97.08%, with precision and recall above 94%. The suggested stack configuration outperformed individual models and other stacking configurations, demonstrating balanced performance with minimal variability. Statistical analysis highlighted the reliability and consistency of the framework under perturbed conditions. The multi-resolution ViT-based framework significantly improves BC classification by integrating multiscale analysis and ensemble decision-making. Its high accuracy and robustness make it a valuable tool for reducing interobserver variability in digital pathology workflows.
Breast cancer is the second-leading cause of cancer-related deaths in women worldwide. Early detection through regular screenings and self-examinations is vital to increasing survival rates especially through histopat...
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ISBN:
(纸本)9798350313345;9798350313338
Breast cancer is the second-leading cause of cancer-related deaths in women worldwide. Early detection through regular screenings and self-examinations is vital to increasing survival rates especially through histopathology image analysis, which helps determine the extent of tumor invasion, the presence of metastasis, and the aggressiveness of the cancer cells. This research paper introduces a novel methodology for classifying Breast Cancer (BC) from histopathology slides using a Vision Transformer (ViT) model. The approach involves gathering a dataset of annotated high-quality histopathology slides, followed by a meticulous pre-processing phase. The ViT model is trained on multiple resolutions of Regions of Interest (ROIs), and a majority voting mechanism is employed for decision-making. Additionally, post-processing techniques, including region growing and fast-marching level set, are applied to enhance the prediction. The proposed framework achieves outstanding results, with the best performance obtained at a ROI scale of 1,024 pixels. At this scale, the model achieves an impressive accuracy of 99.42%, precision and recall of 98.86% and 98.84%, respectively, and a balanced accuracy of 99.23%. The research contributes to the advancement of computer-aided diagnosis in histopathology, particularly in BC classification.
Modern Computed Tomography scans are acquired down to a slice thickness of 0.5 mm thus yielding a huge number of 2D slices to be examined by the physician. Hence the need for automated computer assisted diagnostics, e...
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ISBN:
(纸本)9788469254158
Modern Computed Tomography scans are acquired down to a slice thickness of 0.5 mm thus yielding a huge number of 2D slices to be examined by the physician. Hence the need for automated computer assisted diagnostics, e.g. in the field of abdominal scans for liver tumor diagnostics and surgery planning, arises. In this work a fully-automated algorithm for robust and accurate segmentation of the liver parenchyma, a prerequisite for liver lobe classification and resection planning, is presented. A first estimate for liver segmentation is achieved by applying a normalized liver model to the CT data. Based on this pre-segmentation parameters for levelset segmentation on a slice-by-slice strategy are assessed, thus enabling a fully-automated segmentation of the liver parenchyma. The slice-by-slice levelset propagation utilizes fast-marching and threshold levelset implementations.
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