Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study

• URL: http://arxiv.org/abs/2107.07761v1
• Date: Fri, 16 Jul 2021 08:36:34 GMT
• Title: Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study
• Authors: Alessio Mascolini, Dario Cardamone, Francesco Ponzio, Santa Di Cataldo, Elisa Ficarra
• Abstract summary: GAN-DL is a Discriminator Learner based on the StyleGAN2 architecture. We show that our technique can be exploited not only for classification tasks, but also to effectively derive a dose response curve.
• Score: 0.41998444721319217
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Networks combined with linear Support Vector Machines enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to previous methods, our deep learning based approach does not require any annotation besides the one that is normally collected during the sample preparation process. We test our technique on the RxRx19a Sars-CoV-2 image collection. The dataset consists of fluorescent images that were generated to assess the ability of regulatory-approved or in late-stage clinical trials compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and HRCE cell lines. We show that our technique can be exploited not only for classification tasks, but also to effectively derive a dose response curve for the tested treatments, in a self-supervised manner. Lastly, we demonstrate its generalization capabilities by successfully addressing a zero-shot learning task, consisting in the categorization of four different cell types of the RxRx1 fluorescent images collection.

Related papers

• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• Semi-supervised GAN for Bladder Tissue Classification in Multi-Domain Endoscopic Images [10.48945682277992]
  We propose a semi-surprised Generative Adrial Network (GAN)-based method composed of three main components. The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively.
  arXiv  Detail & Related papers  (2022-12-21T21:32:36Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs [8.596819713822477]
  We present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal plexuses and 2) a suite of physics-based image augmentations.
  arXiv  Detail & Related papers  (2022-07-22T14:22:22Z)
• Stain based contrastive co-training for histopathological image analysis [61.87751502143719]
  We propose a novel semi-supervised learning approach for classification of histovolution images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. We evaluate our approach in clear cell renal cell and prostate carcinomas, and demonstrate improvement over state-of-the-art semi-supervised learning methods.
  arXiv  Detail & Related papers  (2022-06-24T22:25:31Z)
• Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment [57.419727894848485]
  A computer-aided diagnosis system is required to assist accurate diagnosis from colonoscopy images. Most previous studies at-tempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. We propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification.
  arXiv  Detail & Related papers  (2021-08-05T09:31:46Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)
• An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics [0.2479153065703935]
  We develop an interpretable, deep learning (DL)-based pipeline which automates the evaluation of FISH images with respect to HER2 gene amplification testing. It mimics the pathological assessment and relies on the detection and localization of interphase nuclei based on instance segmentation networks. It localizes and classifies fluorescence signals within each nucleus with the help of image classification and object detection convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2020-05-25T12:14:38Z)
• Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks [4.294650528226683]
  COVID-19 is a respiratory syndrome that resembles pneumonia. Scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections. This article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches.
  arXiv  Detail & Related papers  (2020-04-23T10:24:34Z)

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.