BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images

• URL: http://arxiv.org/abs/2401.09791v1
• Date: Thu, 18 Jan 2024 08:23:29 GMT
• Title: BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images
• Authors: Negar Golestani, Aihui Wang, Gregory R Bean, and Mirabela Rusu
• Abstract summary: This study introduces a deep learning-based image registration approach trained on mono-modal synthetic image pairs. The models were trained using data from 50 women who received neoadjuvant chemotherapy and underwent surgery.
• Score: 0.05454343470301196
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A standard treatment protocol for breast cancer entails administering neoadjuvant therapy followed by surgical removal of the tumor and surrounding tissue. Pathologists typically rely on cabinet X-ray radiographs, known as Faxitron, to examine the excised breast tissue and diagnose the extent of residual disease. However, accurately determining the location, size, and focality of residual cancer can be challenging, and incorrect assessments can lead to clinical consequences. The utilization of automated methods can improve the histopathology process, allowing pathologists to choose regions for sampling more effectively and precisely. Despite the recognized necessity, there are currently no such methods available. Training such automated detection models require accurate ground truth labels on ex-vivo radiology images, which can be acquired through registering Faxitron and histopathology images and mapping the extent of cancer from histopathology to x-ray images. This study introduces a deep learning-based image registration approach trained on mono-modal synthetic image pairs. The models were trained using data from 50 women who received neoadjuvant chemotherapy and underwent surgery. The results demonstrate that our method is faster and yields significantly lower average landmark error ($2.1\pm1.96$ mm) over the state-of-the-art iterative ($4.43\pm4.1$ mm) and deep learning ($4.02\pm3.15$ mm) approaches. Improved performance of our approach in integrating radiology and pathology information facilitates generating large datasets, which allows training models for more accurate breast cancer detection.

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)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images [49.24576562557866]
  We propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods.
  arXiv  Detail & Related papers  (2022-11-01T07:41:03Z)
• EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation [29.162527503224364]
  Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. New publicly available Enteroscope Biopsy histopathology enteroscope biopsy dataset (EBHI) is published in this paper. Traditional machine learning methods achieve maximum accuracy of 76.02% and deep learning method achieves a maximum accuracy of 95.37%.
  arXiv  Detail & Related papers  (2022-02-17T09:53:02Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Bridging the gap between prostate radiology and pathology through machine learning [2.090877308669147]
  We compare different labeling strategies, namely, pathology-confirmed radiologist labels, pathologist labels on whole-mount histopathology images, and lesion-level and pixel-level digital pathologist labels. We analyse the effects these labels have on the performance of the trained machine learning models.
  arXiv  Detail & Related papers  (2021-12-03T21:38:20Z)
• 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)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• 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)

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.