A QuadTree Image Representation for Computational Pathology
- URL: http://arxiv.org/abs/2108.10873v1
- Date: Tue, 24 Aug 2021 17:53:19 GMT
- Title: A QuadTree Image Representation for Computational Pathology
- Authors: Rob Jewsbury, Abhir Bhalerao, Nasir Rajpoot
- Abstract summary: Histopathology images are large and need to be split up into image tiles or patches so modern convolutional neural networks (CNNs) can process them.
We present a method to generate an interpretable image representation of computational pathology images using quadtrees and a pipeline.
- Score: 1.8047694351309205
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The field of computational pathology presents many challenges for computer
vision algorithms due to the sheer size of pathology images. Histopathology
images are large and need to be split up into image tiles or patches so modern
convolutional neural networks (CNNs) can process them. In this work, we present
a method to generate an interpretable image representation of computational
pathology images using quadtrees and a pipeline to use these representations
for highly accurate downstream classification. To the best of our knowledge,
this is the first attempt to use quadtrees for pathology image data. We show it
is highly accurate, able to achieve as good results as the currently widely
adopted tissue mask patch extraction methods all while using over 38% less
data.
Related papers
- From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis [81.19923502845441]
We develop a graph-based framework that constructs WSI graph representations.
We build tissue representations (nodes) that follow biological boundaries rather than arbitrary patches.
In our method's final step, we solve the diagnostic task through a graph attention network.
arXiv Detail & Related papers (2025-03-14T20:15:04Z) - μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation [2.012378666405002]
X-ray computed microtomography (mu-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples.
extracting relevant information from 3D images requires semantic segmentation of the regions of interest.
We propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus.
arXiv Detail & Related papers (2024-06-24T15:29:08Z) - Two-Stream Graph Convolutional Network for Intra-oral Scanner Image
Segmentation [133.02190910009384]
We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes.
Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
arXiv Detail & Related papers (2022-04-19T10:41:09Z) - Incremental Cross-view Mutual Distillation for Self-supervised Medical
CT Synthesis [88.39466012709205]
This paper builds a novel medical slice to increase the between-slice resolution.
Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy.
Our method outperforms state-of-the-art algorithms by clear margins.
arXiv Detail & Related papers (2021-12-20T03:38:37Z) - Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image
Synthesis [65.47507533905188]
Conditional generative adversarial networks have been applied to generate synthetic histopathology images.
We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
arXiv Detail & Related papers (2021-10-27T18:54:25Z) - HistoTransfer: Understanding Transfer Learning for Histopathology [9.231495418218813]
We compare the performance of features extracted from networks trained on ImageNet and histopathology data.
We investigate if features learned using more complex networks lead to gain in performance.
arXiv Detail & Related papers (2021-06-13T18:55:23Z) - Wide & Deep neural network model for patch aggregation in CNN-based
prostate cancer detection systems [51.19354417900591]
Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020.
To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images.
Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
arXiv Detail & Related papers (2021-05-20T18:13:58Z) - A Petri Dish for Histopathology Image Analysis [25.424907516487327]
We introduce a minimalist histopathology image analysis dataset (MHIST)
MHIST is a binary classification dataset of 3,152 fixed-size images of colorectal polyps.
MHIST occupies less than 400 MB of disk space, and a ResNet-18 baseline can be trained to convergence on MHIST in just 6 minutes.
arXiv Detail & Related papers (2021-01-29T02:01:45Z) - Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.
In this paper, we develop a novel generative method named generative adversarial U-Net.
Our newly designed model is domain-free and generalizable to various medical images.
arXiv Detail & Related papers (2021-01-12T23:02:26Z) - Neural Sparse Representation for Image Restoration [116.72107034624344]
Inspired by the robustness and efficiency of sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks.
Our method structurally enforces sparsity constraints upon hidden neurons.
Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks.
arXiv Detail & Related papers (2020-06-08T05:15:17Z)
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.