MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening

• URL: http://arxiv.org/abs/2308.01057v1
• Date: Wed, 2 Aug 2023 10:10:22 GMT
• Title: MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening
• Authors: Yijun Yang, Shujun Wang, Lihao Liu, Sarah Hickman, Fiona J Gilbert, Carola-Bibiane Sch\"onlieb, Angelica I. Aviles-Rivero
• Abstract summary: Mammography poses challenges due to the high variability and patterns in mammograms. MammoDG is a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data.
• Score: 4.587250201300601
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer is a major cause of cancer death among women, emphasising the importance of early detection for improved treatment outcomes and quality of life. Mammography, the primary diagnostic imaging test, poses challenges due to the high variability and patterns in mammograms. Double reading of mammograms is recommended in many screening programs to improve diagnostic accuracy but increases radiologists' workload. Researchers explore Machine Learning models to support expert decision-making. Stand-alone models have shown comparable or superior performance to radiologists, but some studies note decreased sensitivity with multiple datasets, indicating the need for high generalisation and robustness models. This work devises MammoDG, a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data. MammoDG leverages multi-view mammograms and a novel contrastive mechanism to enhance generalisation capabilities. Extensive validation demonstrates MammoDG's superiority, highlighting the critical importance of domain generalisation for trustworthy mammography analysis in imaging protocol variations.

Related papers

• Deep BI-RADS Network for Improved Cancer Detection from Mammograms [3.686808512438363]
  We introduce a novel multi-modal approach that combines textual BI-RADS lesion descriptors with visual mammogram content. Our method employs iterative attention layers to effectively fuse these different modalities. Experiments on the CBIS-DDSM dataset demonstrate substantial improvements across all metrics.
  arXiv  Detail & Related papers  (2024-11-16T21:32:51Z)
• Breast Ultrasound Report Generation using LangChain [58.07183284468881]
  We propose the integration of multiple image analysis tools through a LangChain using Large Language Models (LLM) into the breast reporting process. Our method can accurately extract relevant features from ultrasound images, interpret them in a clinical context, and produce comprehensive and standardized reports.
  arXiv  Detail & Related papers  (2023-12-05T00:28:26Z)
• 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)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection [49.14070210387509]
  We propose an Anatomy-aware Graph convolutional Network (AGN) for mammogram mass detection. AGN is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-21T06:48:34Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• 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)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• 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)
• Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention [2.7490008316742096]
  Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. Among diverse types of breast abnormalities, masses are the most important clinical findings of breast carcinomas. We present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms.
  arXiv  Detail & Related papers  (2020-02-27T13:16:22Z)
• 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.