CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

• URL: http://arxiv.org/abs/2112.01330v1
• Date: Thu, 2 Dec 2021 15:31:51 GMT
• Title: CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
• Authors: Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith
• Abstract summary: Interval and large invasive breast cancers are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues. We introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking.
• Score: 8.0701718204815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.

Related papers

• Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
  We propose a novel text-guided learning method to achieve highly accurate cancer detection results. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
  arXiv  Detail & Related papers  (2024-05-23T07:03:38Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Longitudinal Mammogram Risk Prediction [6.28887425442237]
  We extend a state-of-the-art machine learning model to ingest an arbitrary number of longitudinal mammograms and predict future breast cancer risk. Our results show that longer histories (e.g., up to four prior annual mammograms) can significantly boost the accuracy of predicting future breast cancer risk.
  arXiv  Detail & Related papers  (2024-04-29T19:52:09Z)
• Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images [0.0]
  Breast cancer (BC) significantly contributes to cancer-related mortality in women. accurately distinguishing malignant mass lesions remains challenging. We propose a novel deep learning approach for BC screening utilizing mammography images.
  arXiv  Detail & Related papers  (2023-10-30T10:22:14Z)
• A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data [82.74877848011798]
  Cancer-Net BCa is a multi-institutional open-source benchmark dataset of volumetric CDI$s$ imaging data of breast cancer patients. Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.
  arXiv  Detail & Related papers  (2023-04-12T05:41:44Z)
• High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection [48.88813637974911]
  Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. High-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms.
  arXiv  Detail & Related papers  (2022-09-20T15:57:12Z)
• DenseNet for Breast Tumor Classification in Mammographic Images [0.0]
  The aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to features selection and extraction; and the classification was carried out by DenseNet architecture.
  arXiv  Detail & Related papers  (2021-01-24T03:30:59Z)
• Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms [0.4297070083645048]
  We present a novel generative adversarial network (GAN) model for data augmentation that can realistically synthesize and remove lesions on mammograms. With self-attention and semi-supervised learning components, the U-net-based architecture can generate high resolution (256x256px) outputs.
  arXiv  Detail & Related papers  (2020-05-29T21:23:00Z)
• Understanding the robustness of deep neural network classifiers for breast cancer screening [52.50078591615855]
  Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. We measure the sensitivity of a radiologist-level screening mammogram image classifier to four commonly studied input perturbations. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features.
  arXiv  Detail & Related papers  (2020-03-23T01:26:36Z)
• Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography [6.323318523772466]
  We introduce a novel top likelihood loss together with a new sampling procedure to select and train the suspected target regions. We firstly test our proposed method on a private dense mammogram dataset. Results show that our proposed method greatly reduce the false positive rate and the specificity is increased by 0.25 on detecting mass type cancer.
  arXiv  Detail & Related papers  (2020-03-01T09:04:24Z)
• 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.