Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers

• URL: http://arxiv.org/abs/2109.05819v1
• Date: Mon, 13 Sep 2021 09:43:12 GMT
• Title: Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers
• Authors: Charlie Saillard, Olivier Dehaene, Tanguy Marchand, Olivier Moindrot, Aur\'elie Kamoun, Benoit Schmauch, Simon Jegou
• Abstract summary: Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC) Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. We leverage recent advances in self-supervised learning by training neural networks on histology images from the TCGA dataset using MoCo V2.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC), and is associated with response to immunotherapy in all solid tumors. Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. Prior deep learning models for MSI detection have relied on neural networks pretrained on ImageNet dataset, which does not contain any medical image. In this study, we leverage recent advances in self-supervised learning by training neural networks on histology images from the TCGA dataset using MoCo V2. We show that these networks consistently outperform their counterparts pretrained using ImageNet and obtain state-of-the-art results for MSI detection with AUCs of 0.92 and 0.83 for CRC and gastric tumors, respectively. These models generalize well on an external CRC cohort (0.97 AUC on PAIP) and improve transfer from one organ to another. Finally we show that predictive image regions exhibit meaningful histological patterns, and that the use of MoCo features highlighted more relevant patterns according to an expert pathologist.

Related papers

• Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors [2.104687387907779]
  We introduce HT-CNNs, an ensemble of Hybrid Transformers and Convolutional Neural Networks optimized through transfer learning for varied brain tumor segmentation. This method captures spatial and contextual details from MRI data, fine-tuned on diverse datasets representing common tumor types. Our findings underscore the potential of transfer learning and ensemble approaches in medical image segmentation, indicating a substantial enhancement in clinical decision-making and patient care.
  arXiv  Detail & Related papers  (2024-12-11T09:52:01Z)
• Enhanced MRI Representation via Cross-series Masking [48.09478307927716]
  Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner. Method achieves state-of-the-art performance on both public and in-house datasets.
  arXiv  Detail & Related papers  (2024-12-10T10:32:09Z)
• Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images [1.48419209885019]
  We propose a novel attention-based adversarially regularized variational graph autoencoder model for breast histological image retrieval. Our top-performing model, trained with UNI features, achieved average mAP/mMV scores of 96.7%/91.5% and 97.6%/94.2% for the BreakHis and BACH datasets, respectively.
  arXiv  Detail & Related papers  (2024-05-07T11:24:37Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification [0.0]
  Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and make a precise diagnosis for effective treatment. Deep learning (DL) models can enhance existing clinical systems and improve patient care by locating regions of interest for physicians. This work uses well-known DL models for the classification and segmentation of mpMRI images to detect PCa.
  arXiv  Detail & Related papers  (2023-10-09T03:00:15Z)
• Automated ensemble method for pediatric brain tumor segmentation [0.0]
  This study introduces a novel ensemble approach using ONet and modified versions of UNet. Data augmentation ensures robustness and accuracy across different scanning protocols. Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-08-14T15:29:32Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology [26.194247664756553]
  Intraoperative diagnosis of skull base tumors can be challenging due to tumor diversity and lack of intraoperative pathology resources. We developed an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses.
  arXiv  Detail & Related papers  (2021-08-08T02:49:29Z)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• 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)

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.