EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks

• URL: http://arxiv.org/abs/2212.00532v2
• Date: Fri, 2 Dec 2022 08:26:21 GMT
• Title: EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
• Authors: Liyu Shi, Xiaoyan Li, Weiming Hu, Haoyuan Chen, Jing Chen, Zizhen Fan, Minghe Gao, Yujie Jing, Guotao Lu, Deguo Ma, Zhiyu Ma, Qingtao Meng, Dechao Tang, Hongzan Sun, Marcin Grzegorzek, Shouliang Qi, Yueyang Teng, Chen Li
• Abstract summary: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. There is a lack of datasets for histological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. This dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.
• Score: 21.17913442266469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.

Related papers

• Multi-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification [7.002657345547741]
  Non-small cell lung cancer (NSCLC) is a predominant cause of cancer mortality worldwide. In this paper, we introduce an innovative integration of multi-modal data, synthesizing fused medical imaging (CT and PET scans) with clinical health records and genomic data. Our research surpasses existing approaches, as evidenced by a substantial enhancement in NSCLC detection and classification precision.
  arXiv  Detail & Related papers  (2024-09-27T12:59:29Z)
• Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
  Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
  arXiv  Detail & Related papers  (2024-07-02T19:30:25Z)
• ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions [7.420919215687338]
  Endometrial cancer is one of the most common tumors in the female reproductive system. This dataset is the first publicly available dataset of endometrial cancer with a large number of multiple images.
  arXiv  Detail & Related papers  (2023-08-16T12:18:27Z)
• 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)
• Lesion detection in contrast enhanced spectral mammography [0.0]
  The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases.
  arXiv  Detail & Related papers  (2022-07-20T06:49:02Z)
• 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)
• Machine Learning Methods for Histopathological Image Analysis: A Review [62.14548392474976]
  Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.
  arXiv  Detail & Related papers  (2021-02-07T19:12:32Z)
• 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)
• 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)
• Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating [0.0]
  Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner. More specifically, we intend to develop experimental Convolutional Neural Network (CNN) architectures and conduct experiments, by tuning their parameters, to investigate their behavior, and to define the optimal setup for the accurate classification.
  arXiv  Detail & Related papers  (2020-03-15T11:46:00Z)

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.