Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification

• URL: http://arxiv.org/abs/2503.19945v2
• Date: Sun, 31 Aug 2025 23:36:46 GMT
• Title: Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification
• Authors: Daniel G. P. Petrini, Hae Yong Kim,
• Abstract summary: Mammography, an X-ray-based imaging technique, plays a crucial role in the early detection of breast cancer.<n>Computer-aided detection and diagnostic methods have been proposed, increasingly leveraging advancements in artificial intelligence and machine learning.<n>In this paper, we evaluate and compare the effectiveness of single-view and multi-view mammogram classification techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mammography, an X-ray-based imaging technique, plays a crucial role in the early detection of breast cancer. Its accuracy heavily depends on expert radiologists, making it essential to minimize interpretation errors. To support radiologists, various computer-aided detection and diagnostic methods have been proposed, increasingly leveraging advancements in artificial intelligence and machine learning. Over recent years, mammogram analysis has evolved significantly - from early patch-based classifiers, which examine only localized regions of images, to full-image classifiers, and later towards multi-view systems that simultaneously integrate multiple perspectives of the mammographic exam for enhanced accuracy. Despite this progression, critical questions remain, such as whether multi-view systems consistently outperform single-view approaches. In this paper, we systematically evaluate and compare the effectiveness of single-view and multi-view mammogram classification techniques. Our research introduces models that achieve superior performance relative to existing state-of-the-art approaches in both single-view and two-view classification scenarios. Furthermore, our findings provide valuable insights into optimal model architectures and effective transfer learning strategies, paving the way for more accurate and efficient mammogram interpretation. The inference code and model are available at https://github.com/dpetrini/multiple-view.

Related papers

• See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement [54.01053990883076]
  SMILE is an anatomy-aware diffusion model that learns how organs are shaped and how they take up contrast.<n>It enhances only clinically relevant regions while leaving all other areas unchanged.<n>It also improves cancer detection from non-contrast CT, raising the F1 score by up to 10 percent.
  arXiv  Detail & Related papers  (2025-12-08T07:48:45Z)
• A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion [5.15423063632115]
  Early and accurate interpretation of screening mammograms is essential for effective breast cancer detection.<n>Existing AI approaches fall short by focusing on single view inputs or single-task outputs.<n>We propose a novel multi-view, multitask hybrid deep learning framework that processes all four standard mammography views.
  arXiv  Detail & Related papers  (2025-07-22T18:52:18Z)
• Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview [3.6586909519359607]
  This paper presents an overview of generalizable and explainable artificial intelligence in deep learning (DL) for medical imaging.<n>We propose to use four CNNs in three medical datasets (brain tumor, skin cancer, and chest x-ray) for medical image classification tasks.
  arXiv  Detail & Related papers  (2025-03-11T13:31:09Z)
• Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification [50.899861205016265]
  We propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification.<n>Our framework introduces two key components into the common MIL model architecture.<n>We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets.
  arXiv  Detail & Related papers  (2025-03-08T04:51:58Z)
• GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis [44.99833362998488]
  We present a novel approach that combines 2D Gaussian splatting with the Transformer UNet architecture for automated skin cancer diagnosis.<n>Our findings illustrate significant advancements in the precision of segmentation and classification.<n>This integration sets new benchmarks in the field and highlights the potential for further research into multi-task medical image analysis methodologies.
  arXiv  Detail & Related papers  (2025-02-23T23:28:47Z)
• Multi-modal Vision Pre-training for Medical Image Analysis [11.569448567735435]
  Self-supervised learning has greatly facilitated medical image analysis by suppressing the training data requirement for real-world applications.<n>We conduct a novel multi-modal image pre-training with three proxy tasks to facilitate the learning of cross-modality representations and correlations.<n>Our method is reported in comparison to state-of-the-art pre-training methods, with Dice Score improvement of 0.28%-14.47% across six segmentation benchmarks and a consistent accuracy boost of 0.65%-18.07% in four individual image classification tasks.
  arXiv  Detail & Related papers  (2024-10-14T15:12:16Z)
• Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography [6.537171378333966]
  Mammo-CLIP is the first multi-modal framework to process multi-view mammograms and corresponding simple texts. To enhance learning efficiency, plug-and-play adapters are added into CLIP image and text encoders for fine-tuning parameters. Study results show that Mammo-CLIP outperforms the state-of-art cross-view transformer in AUC.
  arXiv  Detail & Related papers  (2024-04-24T16:07:31Z)
• Parkinson's Disease Classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features [5.660131312162423]
  Parkinson's Disease (PD) affects millions globally, impacting movement. Prior research utilized deep learning for PD prediction, primarily focusing on medical images, neglecting the data's underlying manifold structure. This work proposes a multimodal approach encompassing both image and non-image features, leveraging contrastive cross-view graph fusion for PD classification.
  arXiv  Detail & Related papers  (2023-11-25T02:32:46Z)
• Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis [1.1098503592431275]
  Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. We propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view. We also propose a simple but novel malignant mammogram synthesis framework for up synthesizing minor class samples.
  arXiv  Detail & Related papers  (2023-09-07T06:33:30Z)
• M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector [13.67324365495568]
  Deep-learning-based object detection methods show promise for improving screening mammography, but high rates of false positives can hinder their effectiveness in clinical practice. We identify three challenges: unlike natural images, a malignant mammogram typically contains only one malignant finding; mammography exams contain two views of each breast, and both views ought to be considered to make a correct assessment. We tackle the three aforementioned challenges by: (1) leveraging Sparse R-CNN and showing that sparse detectors are more appropriate than dense detectors for mammography; (2) including a multi-view cross-attention module to synthesize information from different views; and (3) incorporating multi-instance
  arXiv  Detail & Related papers  (2023-08-11T23:59:47Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels [54.58539616385138]
  We introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA) First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features.
  arXiv  Detail & Related papers  (2022-09-27T15:50:31Z)
• Mammograms Classification: A Review [0.0]
  Mammogram images have been utilized in developing computer-aided diagnosis systems. Researchers have proved that artificial intelligence with its emerging technologies can be used in the early detection of the disease.
  arXiv  Detail & Related papers  (2022-03-04T19:22:35Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning [47.30824944649112]
  A new contrastive learning scheme is developed to augment the generalization capability of deep learning model to various vendors with limited resources. The backbone network is trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles. The experimental results suggest that our approach can effectively improve detection performance on both seen and unseen domains.
  arXiv  Detail & Related papers  (2021-11-21T14:29:50Z)
• 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)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)
• Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification [63.44396343014749]
  We propose a new margin-based surrogate loss function for the AUC score. It is more robust than the commonly used. square loss while enjoying the same advantage in terms of large-scale optimization. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets.
  arXiv  Detail & Related papers  (2020-12-06T03:41:51Z)
• Using Machine Learning to Automate Mammogram Images Analysis [12.19801103274363]
  Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate. A computer-aided automatic mammogram analysis system is proposed to process the mammogram images and automatically discriminate them as either normal or cancerous.
  arXiv  Detail & Related papers  (2020-12-06T00:10:18Z)
• 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)

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.