SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRI

• URL: http://arxiv.org/abs/2306.16714v1
• Date: Thu, 29 Jun 2023 06:22:50 GMT
• Title: SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRI
• Authors: Yuming Zhong and Yi Wang
• Abstract summary: The segmentation of cancerous regions is essential for the subsequent analysis of breast MRI. We propose a weakly-supervised strategy using extreme points as annotations for breast cancer segmentation. Experimental results demonstrate our method effectively fine-tunes the network by using the SimPLe strategy.
• Score: 4.689359813220365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in the screening and prognosis assessment of high-risk breast cancer. The segmentation of cancerous regions is essential useful for the subsequent analysis of breast MRI. To alleviate the annotation effort to train the segmentation networks, we propose a weakly-supervised strategy using extreme points as annotations for breast cancer segmentation. Without using any bells and whistles, our strategy focuses on fully exploiting the learning capability of the routine training procedure, i.e., the train - fine-tune - retrain process. The network first utilizes the pseudo-masks generated using the extreme points to train itself, by minimizing a contrastive loss, which encourages the network to learn more representative features for cancerous voxels. Then the trained network fine-tunes itself by using a similarity-aware propagation learning (SimPLe) strategy, which leverages feature similarity between unlabeled and positive voxels to propagate labels. Finally the network retrains itself by employing the pseudo-masks generated using previous fine-tuned network. The proposed method is evaluated on our collected DCE-MRI dataset containing 206 patients with biopsy-proven breast cancers. Experimental results demonstrate our method effectively fine-tunes the network by using the SimPLe strategy, and achieves a mean Dice value of 81%.

Related papers

• Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive Learning [4.066789590650407]
  We propose a novel deep learning framework combining the Segment Anything Model (SAM) with Forward-Forward Contrastive Learning (FFCL)<n>Our approach achieved an AUC of 0.8455 for margin classification and segmented margins with a 27.4% improvement over baseline models, while reducing inference time to 47 milliseconds per image.
  arXiv  Detail & Related papers  (2025-06-26T04:46:28Z)
• Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and Convolutional Neural Network [3.3670613441132993]
  The scarcity of high-quality curated labeled medical training data remains one of the major limitations in applying artificial intelligence (AI) systems to breast cancer diagnosis. We propose a novel method that leverages self-supervised learning (SSL) and a deep hybrid model, named textbfHybMNet, which combines local self-attention and fine-grained feature extraction. The proposed method was evaluated for its ability to detect breast cancer by distinguishing between benign (normal) and malignant mammograms.
  arXiv  Detail & Related papers  (2025-04-28T15:23:28Z)
• Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
  We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-11T15:46:00Z)
• CT-based brain ventricle segmentation via diffusion Schrödinger Bridge without target domain ground truths [0.9720086191214947]
  Efficient and accurate brain ventricle segmentation from clinical CT scans is critical for emergency surgeries like ventriculostomy. We introduce a novel uncertainty-aware ventricle segmentation technique without the need of CT segmentation ground truths. Our method employs the diffusion Schr"odinger Bridge and an attention recurrent residual U-Net to capitalize on unpaired CT and MRI scans.
  arXiv  Detail & Related papers  (2024-05-28T15:17:58Z)
• Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification [0.0]
  Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and make a precise diagnosis for effective treatment. Deep learning (DL) models can enhance existing clinical systems and improve patient care by locating regions of interest for physicians. This work uses well-known DL models for the classification and segmentation of mpMRI images to detect PCa.
  arXiv  Detail & Related papers  (2023-10-09T03:00:15Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging [0.6091702876917281]
  The article introduces an efficient residual cross-spatial attention guided inception U-Net (RCA-IUnet) model with minimal training parameters for tumor segmentation. The RCA-IUnet model follows U-Net topology with residual inception depth-wise separable convolution and hybrid pooling layers. Cross-spatial attention filters are added to suppress the irrelevant features and focus on the target structure.
  arXiv  Detail & Related papers  (2021-08-05T10:35:06Z)
• Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation [3.8791511769387634]
  We developed a new deep learning CBCT lung tumor segmentation method. Key idea of our approach is to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network training. We accomplish this by training an end-to-end network comprised of unpaired domain adaptation (UDA) and cross-domain segmentation distillation networks (SDN) using unpaired CBCT and MRI datasets.
  arXiv  Detail & Related papers  (2021-02-17T03:52:02Z)
• Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention [2.204996105506197]
  We propose a 2.5D Convolutional Neural Network (CNN) to handle the difference of inplane and through-plane resolution. We also propose a spatial attention module to enable the network to focus on small target, and use channel attention to further improve the segmentation performance.
  arXiv  Detail & Related papers  (2021-01-27T08:20:49Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.