MyData: A Comprehensive Database of Mycetoma Tissue Microscopic Images for Histopathological Analysis

• URL: http://arxiv.org/abs/2410.12833v1
• Date: Wed, 02 Oct 2024 19:56:56 GMT
• Title: MyData: A Comprehensive Database of Mycetoma Tissue Microscopic Images for Histopathological Analysis
• Authors: Hyam Omar Ali, Romain Abraham, Guillaume Desoubeaux, Ahmed Fahal, Clovis Tauber,
• Abstract summary: Mycetoma is a chronic and neglected inflammatory disease prevalent in tropical and subtropical regions. The disease is classified into two types based on the causative microorganisms: eumycetoma (fungal) and actinomycetoma (bacterial)
• Score: 0.34619585024232546
• License:
• Abstract: Mycetoma is a chronic and neglected inflammatory disease prevalent in tropical and subtropical regions. It can lead to severe disability and social stigma. The disease is classified into two types based on the causative microorganisms: eumycetoma (fungal) and actinomycetoma (bacterial). Effective treatment strategies depend on accurately identifying the causative agents. Current identification methods include molecular, cytological, and histopathological techniques, as well as grain culturing. Among these, histopathological techniques are considered optimal for use in endemic areas, but they require expert pathologists for accurate identification, which can be challenging in rural areas lacking such expertise. The advent of digital pathology and automated image analysis algorithms offers a potential solution. This report introduces a novel dataset designed for the automated detection and classification of mycetoma using histopathological images. It includes the first database of microscopic images of mycetoma tissue, detailing the entire pipeline from species distribution and patient sampling to acquisition protocols through histological procedures. The dataset consists of images from 142 patients, totalling 864 images, each annotated with binary masks indicating the presence of grains, facilitating both detection and segmentation tasks.

Related papers

• Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits [0.7968706282619793]
  Multi multiplexed imaging allows for simultaneous visualization of multiple biomarkers in a single section. Data from multiplexed imaging requires sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. PathML is an AI-powered platform that streamlines image analysis, making complex interpretation accessible for clinical and research settings.
  arXiv  Detail & Related papers  (2024-11-01T18:02:41Z)
• From Pixel to Slide image: Polarization Modality-based Pathological Diagnosis Using Representation Learning [9.326969394501958]
  Pathologically, thyroid tumors pose diagnostic challenges due to improper specimen sampling. We have designed a three-stage model using representation learning to integrate pixel-level and slice-level annotations for distinguishing thyroid tumors.
  arXiv  Detail & Related papers  (2024-01-03T02:01:09Z)
• PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation [41.80715213373843]
  Pathologic myopia (PM) is a common myopic retinal degeneration suffered by highly blinding population. This paper provides insights about PALM, our open fundus imaging dataset for pathological recognition and anatomical structure annotation.
  arXiv  Detail & Related papers  (2023-05-13T02:00:06Z)
• 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)
• Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images [0.0]
  Microvascular diseases are assessed by visual inspection of retinal images. This can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease.
  arXiv  Detail & Related papers  (2022-02-20T01:29:36Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading [12.222174729732904]
  We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide images. This dataset is meant for training deep neural networks for colorectal polyps classification and adenomas grading.
  arXiv  Detail & Related papers  (2021-01-25T10:18:44Z)
• Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends [0.33554367023486936]
  Histopathology images are captured by a microscope to locate, examine, and classify many diseases. Analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented.
  arXiv  Detail & Related papers  (2020-11-10T07:31:05Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

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.