Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

• URL: http://arxiv.org/abs/2011.05790v1
• Date: Tue, 10 Nov 2020 07:31:05 GMT
• Title: Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends
• Authors: Naira Elazab, Hassan Soliman, Shaker El-Sappagh, S. M. Riazul Islam, and Mohammed Elmogy
• Abstract summary: 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.
• Score: 0.33554367023486936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.

Related papers

• Knowledge-enhanced Visual-Language Pretraining for Computational Pathology [68.6831438330526]
  We consider the problem of visual representation learning for computational pathology, by exploiting large-scale image-text pairs gathered from public resources. We curate a pathology knowledge tree that consists of 50,470 informative attributes for 4,718 diseases requiring pathology diagnosis from 32 human tissues. All codes, models and the pathology knowledge tree will be released to the research community.
  arXiv  Detail & Related papers  (2024-04-15T17:11:25Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• On Image Search in Histopathology [0.0]
  We review the latest developments in image search technologies for histopathology. We offer a concise overview tailored for computational pathology researchers seeking effective, fast and efficient image search methods in their work.
  arXiv  Detail & Related papers  (2024-01-14T12:38:49Z)
• Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis [59.35504779947686]
  GPT-4V is OpenAI's newest model for multimodal medical diagnosis. Our evaluation encompasses 17 human body systems. GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy. It faces significant challenges in disease diagnosis and generating comprehensive reports.
  arXiv  Detail & Related papers  (2023-10-15T18:32:27Z)
• Deepfake histological images for enhancing digital pathology [0.40631409309544836]
  We develop a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigate the ability of this framework in synthesizing realistic prostate and colon tissue images. We extend the approach to significantly more complex images from colon biopsies and show that the complex microenvironment in such tissues can also be reproduced.
  arXiv  Detail & Related papers  (2022-06-16T17:11:08Z)
• Recent advances and clinical applications of deep learning in medical image analysis [7.132678647070632]
  We reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images.
  arXiv  Detail & Related papers  (2021-05-27T18:05:12Z)
• 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)
• Weakly supervised multiple instance learning histopathological tumor segmentation [51.085268272912415]
  We propose a weakly supervised framework for whole slide imaging segmentation. We exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
  arXiv  Detail & Related papers  (2020-04-10T13:12:47Z)
• Deep neural network models for computational histopathology: A survey [1.2891210250935146]
  deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used. We highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.
  arXiv  Detail & Related papers  (2019-12-28T01:04:25Z)

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.