Inference of captions from histopathological patches

• URL: http://arxiv.org/abs/2202.03432v1
• Date: Mon, 7 Feb 2022 10:02:38 GMT
• Title: Inference of captions from histopathological patches
• Authors: Masayuki Tsuneki, Fahdi Kanavati
• Abstract summary: We make the captioned dataset of 262K patches publicly available. We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational histopathology has made significant strides in the past few years, slowly getting closer to clinical adoption. One area of benefit would be the automatic generation of diagnostic reports from H\&E-stained whole slide images which would further increase the efficiency of the pathologists' routine diagnostic workflows. In this study, we compiled a dataset (PatchGastricADC22) of histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens, which we extracted from diagnostic reports and paired with patches extracted from the associated whole slide images. The dataset contains a variety of gastric adenocarcinoma subtypes. We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results. We make the captioned dataset of 262K patches publicly available.

Related papers

• WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images [5.960501267687475]
  We investigate how to generate pathology reports given whole slide images. We curated the largest WSI-text dataset (PathText) On the model end, we propose the multiple instance generative model (MI-Gen)
  arXiv  Detail & Related papers  (2023-11-27T05:05:41Z)
• ERCPMP: An Endoscopic Image and Video Dataset for Colorectal Polyps Morphology and Pathology [0.0]
  This dataset contains demographic, morphological and pathological data, endoscopic images and videos of 191 patients with colorectal polyps. Pathological data includes the diagnosis of the polyps including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory and Adenocarcinoma with Dysplasia Grade & Differentiation.
  arXiv  Detail & Related papers  (2023-07-28T09:52:20Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• 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)
• Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies [57.712855968194305]
  We propose a prediction method that can cope with a high number of different scanning sites and a low number of samples per site. Results show that they improve the prediction accuracy for steatosis after 48 weeks from imaging data acquired at an initial visit and 12-weeks follow-up in liver disease.
  arXiv  Detail & Related papers  (2021-11-15T09:40:54Z)
• BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images [69.41441138140895]
  This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
  arXiv  Detail & Related papers  (2021-10-05T19:14:46Z)
• 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)
• Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images [2.864559331994679]
  Digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. Recent developments in deep-learning methods have enabled us to automatically detect and characterize the tumor regions in pathology images at large scale.
  arXiv  Detail & Related papers  (2020-12-09T05:56:41Z)
• Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations [70.0118756144807]
  This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation and segmentation of radiographs. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2%, compared to 27.0% and 34.9% respectively in control images.
  arXiv  Detail & Related papers  (2020-05-08T02:16:17Z)
• Representation Learning of Histopathology Images using Graph Neural Networks [12.427740549056288]
  We propose a two-stage framework for WSI representation learning. We sample relevant patches using a color-based method and use graph neural networks to learn relations among sampled patches to aggregate the image information into a single vector representation. We demonstrate the performance of our approach for discriminating two sub-types of lung cancers, Lung Adenocarcinoma (LUAD) & Lung Squamous Cell Carcinoma (LUSC)
  arXiv  Detail & Related papers  (2020-04-16T00:09:20Z)
• 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)

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.