Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation

• URL: http://arxiv.org/abs/2209.10478v1
• Date: Wed, 21 Sep 2022 16:29:01 GMT
• Title: Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation
• Authors: Yuanhong Chen, Hu Wang, Chong Wang, Yu Tian, Fengbei Liu, Michael Elliott, Davis J. McCarthy, Helen Frazer, Gustavo Carneiro
• Abstract summary: We propose a new multi-view global-local analysis method that mimics the radiologist's reading procedure. Our model outperforms competing methods in terms of classification accuracy and generalisation.
• Score: 23.51938491048481
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views. These multiple related images provide complementary diagnostic information and can improve the radiologist's classification accuracy. Unfortunately, most existing deep learning systems, trained with globally-labelled images, lack the ability to jointly analyse and integrate global and local information from these multiple views. By ignoring the potentially valuable information present in multiple images of a screening episode, one limits the potential accuracy of these systems. Here, we propose a new multi-view global-local analysis method that mimics the radiologist's reading procedure, based on a global consistency learning and local co-occurrence learning of ipsilateral views in mammograms. Extensive experiments show that our model outperforms competing methods, in terms of classification accuracy and generalisation, on a large-scale private dataset and two publicly available datasets, where models are exclusively trained and tested with global labels.

Related papers

• CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning [76.98039909663756]
  We present CheXWorld, the first effort towards a self-supervised world model for radiographic images. Our work develops a unified framework that simultaneously models three aspects of medical knowledge essential for qualified radiologists.
  arXiv  Detail & Related papers  (2025-04-18T17:50:43Z)
• Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image Analysis [16.268045905735818]
  CMSwinKAN is a contrastive-learning-based multi-scale feature fusion model tailored for pathological image classification. We introduce a soft voting mechanism guided by clinical insights to seamlessly bridge patch-level predictions to whole slide image-level classifications. Results demonstrate that CMSwinKAN performs better than existing state-of-the-art pathology-specific models pre-trained on large datasets.
  arXiv  Detail & Related papers  (2025-04-18T15:39:46Z)
• AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images [7.647881928269929]
  We propose an Anatomy-driven self-supervised framework for enhancing Fine-grained Representation in radiographic image analysis (AFiRe) The core idea of AFiRe is to align the anatomical consistency with the unique token-processing characteristics of Vision Transformer. Experimental results show that AFiRe provides robust anatomical discrimination, achieving more cohesive feature clusters compared to state-of-the-art contrastive learning methods.
  arXiv  Detail & Related papers  (2025-04-15T08:29:54Z)
• Visual Prompt Engineering for Vision Language Models in Radiology [0.17183214167143138]
  Contrastive Language-Image Pretraining (CLIPP) offers a solution by enabling zero-shot classification through large-scale pretraining. Visual markers improve AUROC2013$ by up to 0.185, highlighting their effectiveness enhancing classification performance. We release our code and preprocessing pipeline, providing a reference point for future work on localized classification in medical imaging.
  arXiv  Detail & Related papers  (2024-08-28T13:53:27Z)
• MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer [0.257133335028485]
  We propose an innovative multi-view network based on transformers to address challenges in mammographic image classification. Our approach introduces a novel shifted window-based dynamic attention block, facilitating the effective integration of multi-view information.
  arXiv  Detail & Related papers  (2024-02-26T04:41:04Z)
• Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model [0.0]
  We propose a framework that uses images generated by a Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning. Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.
  arXiv  Detail & Related papers  (2023-05-16T14:08:24Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• Cross Modal Global Local Representation Learning from Radiology Reports and X-Ray Chest Images [0.0]
  Deep learning models can be applied successfully in real-work problems; however, training most of these models requires massive data. Recent methods use language and vision, but unfortunately, they rely on datasets that are not usually publicly available. This paper paves the way for further research in the multimodal language-vision domain for radiology.
  arXiv  Detail & Related papers  (2023-01-26T06:02:28Z)
• Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology [9.037868656840736]
  In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features. This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions. Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
  arXiv  Detail & Related papers  (2022-04-18T13:31:53Z)
• Multi-View Hypercomplex Learning for Breast Cancer Screening [7.147856898682969]
  Traditionally, deep learning methods for breast cancer classification perform a single-view analysis. radiologists simultaneously analyze all four views that compose a mammography exam. We propose a methodological approach for multi-view breast cancer classification based on parameterized hypercomplex neural networks.
  arXiv  Detail & Related papers  (2022-04-12T13:32:31Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• Weakly supervised multiple instance learning histopathological tumor segmentation [51.085268272912415]
  We propose a weakly supervised framework for whole slide imaging segmentation. We exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
  arXiv  Detail & Related papers  (2020-04-10T13:12:47Z)

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.