Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques

• URL: http://arxiv.org/abs/2311.11065v1
• Date: Sat, 18 Nov 2023 13:08:09 GMT
• Title: Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques
• Authors: Leon Hamnett, Mary Adewunmi, Modinat Abayomi, Kayode Raheem, and Fahad Ahmed
• Abstract summary: This paper introduces a methodology that combines automated image augmentation selection (RandAugment) with search strategies (Tree-based Parzen Estimator) We empirically validate our approach on breast cancer histology slides, focusing on the segmentation of cancer cells. Our results show that the proposed methodology leads to segmentation models that are more resilient to variations in histology slides.
• Score: 3.495246564946556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer remains a critical global health challenge, necessitating early and accurate detection for effective treatment. This paper introduces a methodology that combines automated image augmentation selection (RandAugment) with search optimisation strategies (Tree-based Parzen Estimator) to identify optimal values for the number of image augmentations and the magnitude of their associated augmentation parameters, leading to enhanced segmentation performance. We empirically validate our approach on breast cancer histology slides, focusing on the segmentation of cancer cells. A comparative analysis of state-of-the-art transformer-based segmentation models is conducted, including SegFormer, PoolFormer, and MaskFormer models, to establish a comprehensive baseline, before applying the augmentation methodology. Our results show that the proposed methodology leads to segmentation models that are more resilient to variations in histology slides whilst maintaining high levels of segmentation performance, and show improved segmentation of the tumour class when compared to previous research. Our best result after applying the augmentations is a Dice Score of 84.08 and an IoU score of 72.54 when segmenting the tumour class. The primary contribution of this paper is the development of a methodology that enhances segmentation performance while ensuring model robustness to data variances. This has significant implications for medical practitioners, enabling the development of more effective machine learning models for clinical applications to identify breast cancer cells from histology slides. Furthermore, the codebase accompanying this research will be released upon publication. This will facilitate further research and application development based on our methodology, thereby amplifying its impact.

Related papers

• Optimizing Lymphocyte Detection in Breast Cancer Whole Slide Imaging through Data-Centric Strategies [0.2796197251957244]
  We develop a data-centric optimization pipeline that attains great lymphocyte detection performance using an off-the-shelf YOLOv5 model. We showcase the interest of this approach in the context of breast cancer where our strategies lead to good lymphocyte detection performances.
  arXiv  Detail & Related papers  (2024-05-22T14:59:50Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Breast Histopathology Image Retrieval by Attention-based Adversarially Regularized Variational Graph Autoencoder with Contrastive Learning-Based Feature Extraction [1.48419209885019]
  This work introduces a novel attention-based adversarially regularized variational graph autoencoder model for breast histological image retrieval. We evaluated the performance of the proposed model on two publicly available datasets of breast cancer histological images.
  arXiv  Detail & Related papers  (2024-05-07T11:24:37Z)
• Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation [10.466349398419846]
  We propose a novel approach that designs brain tumour growth Partial Differential Equation (PDE) models as a regularisation with deep learning. Our method introduces tumour growth PDE models directly into the segmentation process, improving accuracy and robustness. We demonstrate the effectiveness of our framework through extensive experiments on the BraTS 2023 dataset.
  arXiv  Detail & Related papers  (2024-03-14T07:21:46Z)
• Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images [0.0]
  Breast cancer (BC) significantly contributes to cancer-related mortality in women. accurately distinguishing malignant mass lesions remains challenging. We propose a novel deep learning approach for BC screening utilizing mammography images.
  arXiv  Detail & Related papers  (2023-10-30T10:22:14Z)
• Automated ensemble method for pediatric brain tumor segmentation [0.0]
  This study introduces a novel ensemble approach using ONet and modified versions of UNet. Data augmentation ensures robustness and accuracy across different scanning protocols. Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-08-14T15:29:32Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• 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)
• Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation [57.78765460295249]
  We develop a novel automatic learning-based data augmentation method for medical image segmentation. In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.
  arXiv  Detail & Related papers  (2020-02-22T14:10:13Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

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.