Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework

• URL: http://arxiv.org/abs/2505.07654v1
• Date: Mon, 12 May 2025 15:22:54 GMT
• Title: Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework
• Authors: Pouya Afshin, David Helminiak, Tongtong Lu, Tina Yen, Julie M. Jorns, Mollie Patton, Bing Yu, Dong Hye Ye,
• Abstract summary: A deep ultraviolet fluorescence scanning microscope (DUV-FSM) enables rapid acquisition of whole surface images (WSIs) for excised tissue.<n>This study introduces a DUV WSI classification framework using a patch-level vision transformer (ViT) model, capturing local and global features.<n>A comprehensive 5-fold cross-validation demonstrates the proposed approach significantly outperforms conventional deep learning methods, achieving a classification accuracy of 98.33%.
• Score: 6.0791593833288085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast-conserving surgery (BCS) aims to completely remove malignant lesions while maximizing healthy tissue preservation. Intraoperative margin assessment is essential to achieve a balance between thorough cancer resection and tissue conservation. A deep ultraviolet fluorescence scanning microscope (DUV-FSM) enables rapid acquisition of whole surface images (WSIs) for excised tissue, providing contrast between malignant and normal tissues. However, breast cancer classification with DUV WSIs is challenged by high resolutions and complex histopathological features. This study introduces a DUV WSI classification framework using a patch-level vision transformer (ViT) model, capturing local and global features. Grad-CAM++ saliency weighting highlights relevant spatial regions, enhances result interpretability, and improves diagnostic accuracy for benign and malignant tissue classification. A comprehensive 5-fold cross-validation demonstrates the proposed approach significantly outperforms conventional deep learning methods, achieving a classification accuracy of 98.33%.

Related papers

• Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography [0.0]
  This study proposes an automated deep learning system for robust binary classification of breast density.<n>We implemented and compared four advanced convolutional neural networks.<n>We developed a novel Combined Focal Label Smoothing Loss function that integrates focal loss, label smoothing, and class-balanced weighting.
  arXiv  Detail & Related papers  (2025-07-08T21:26:33Z)
• Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification [0.0]
  Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer. We present a novel approach, Supervised Contrastive Vision Transformer (SupCon-ViT), for improving the classification of IDC.
  arXiv  Detail & Related papers  (2024-04-17T03:51:55Z)
• Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors [0.33384431000910975]
  We evaluate four popular few shot learning methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme.
  arXiv  Detail & Related papers  (2023-11-13T10:17:00Z)
• Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging [37.21885467891782]
  Surgery for gliomas (intrinsic brain tumors) is challenging due to the infiltrative nature of the lesion. No real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. We build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance.
  arXiv  Detail & Related papers  (2023-02-06T15:52:03Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• 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)
• 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)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

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.