Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning

• URL: http://arxiv.org/abs/2305.20058v1
• Date: Wed, 31 May 2023 17:33:28 GMT
• Title: Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning
• Authors: Imane Nedjar, Mohammed Brahimi, Said Mahmoudi, Khadidja Abi Ayad, Mohammed Amine Chikh
• Abstract summary: This study aims to highlight the regions of interest used by a convolutional neural network (CNN) for classifying histological images as benign or malignant. We employed the VGG19 architecture and tested three visualization methods: Gradient, LRP Z, and LRP Epsilon. Based on the results obtained, the Gradient visualization method and the MeanShift selection method yielded satisfactory outcomes for visualizing the images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer aided detection and diagnosis systems based on deep learning have shown promising performance in breast cancer detection. However, there are cases where the obtained results lack justification. In this study, our objective is to highlight the regions of interest used by a convolutional neural network (CNN) for classifying histological images as benign or malignant. We compare these regions with the regions identified by pathologists. To achieve this, we employed the VGG19 architecture and tested three visualization methods: Gradient, LRP Z, and LRP Epsilon. Additionally, we experimented with three pixel selection methods: Bins, K-means, and MeanShift. Based on the results obtained, the Gradient visualization method and the MeanShift selection method yielded satisfactory outcomes for visualizing the images.

Related papers

• Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example [40.3927727959038]
  This paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images. It enables the rapid and automatic classification of pathological images into benign and malignant groups. It demonstrates that the proposed method effectively enhances the accuracy in classifying pathological images of breast cancer.
  arXiv  Detail & Related papers  (2024-04-12T07:08:05Z)
• Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer [7.607669435880715]
  Graph based features are applied to gastric cancer histopathology microscopic image analysis. It is found that using U-Net to segment tissue areas, then extracting graph based features, and finally using RBF SVM classifier gives the optimal results with 94.29%.
  arXiv  Detail & Related papers  (2022-05-17T16:16:29Z)
• Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation [49.59567529191423]
  We propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D.
  arXiv  Detail & Related papers  (2022-03-07T19:02:26Z)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis [17.936019428281586]
  In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output. We introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset.
  arXiv  Detail & Related papers  (2021-06-13T17:25:21Z)
• Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images [84.03487786163781]
  We develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection modules. Our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation.
  arXiv  Detail & Related papers  (2021-04-05T13:15:22Z)
• Attention Model Enhanced Network for Classification of Breast Cancer Image [54.83246945407568]
  AMEN is formulated in a multi-branch fashion with pixel-wised attention model and classification submodular. To focus more on subtle detail information, the sample image is enhanced by the pixel-wised attention map generated from former branch. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed method under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:44:21Z)
• Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography [18.979126709943085]
  We introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results. Our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner. Experimental results show that DualCoreNet achieves the best mammography segmentation and classification simultaneously, outperforming recent state-of-the-art models.
  arXiv  Detail & Related papers  (2020-08-07T02:23:36Z)
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
  We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
  arXiv  Detail & Related papers  (2020-04-17T11:55:03Z)
• Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning [2.4178424543973267]
  Computer-aided pathology to analyze microscopic histopathology images for diagnosis can bring the cost and delays of diagnosis down. Deep learning in histopathology has attracted attention over the last decade of achieving state-of-the-art performance in classification and localization tasks. We present classification and localization results on two publicly available BreakHIS and BACH dataset.
  arXiv  Detail & Related papers  (2020-02-16T10:29:16Z)

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.