VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology

• URL: http://arxiv.org/abs/2303.04696v1
• Date: Wed, 8 Mar 2023 16:35:47 GMT
• Title: VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology
• Authors: Ramin Nakhli, Allen Zhang, Hossein Farahani, Amirali Darbandsari, Elahe Shenasa, Sidney Thiessen, Katy Milne, Jessica McAlpine, Brad Nelson, C Blake Gilks, Ali Bashashati
• Abstract summary: We propose a self-supervised framework (VOLTA) for cell representation learning in histopathology images. We subjected our model to extensive experiments on the data collected from multiple institutions around the world. To showcase the potential power of our proposed framework, we applied VOLTA to ovarian and endometrial cancers with very small sample sizes.
• Score: 0.3436781233454516
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manual cell annotations is time-consuming due to the large number of cells. In this paper, we propose a self-supervised framework (VOLTA) for cell representation learning in histopathology images using a novel technique that accounts for the cell's mutual relationship with its environment for improved cell representations. We subjected our model to extensive experiments on the data collected from multiple institutions around the world comprising of over 700,000 cells, four cancer types, and cell types ranging from three to six categories for each dataset. The results show that our model outperforms the state-of-the-art models in cell representation learning. To showcase the potential power of our proposed framework, we applied VOLTA to ovarian and endometrial cancers with very small sample sizes (10-20 samples) and demonstrated that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide novel insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised deep learning models that require large sample sizes for training, we provide a framework that can empower new discoveries without any annotation data in situations where sample sizes are limited.

Related papers

• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• A Nasal Cytology Dataset for Object Detection and Deep Learning [0.0]
  We present the first dataset of rhino-cytological field images: the NCD (Nasal Cytology dataset) The real distribution of the cytotypes, populating the nasal mucosa has been replicated, sampling images from slides of clinical patients, and manually annotating each cell found on them. This work contributes to some of open challenges by presenting a novel machine learning-based approach to aid the automated detection and classification of nasal mucosa cells.
  arXiv  Detail & Related papers  (2024-04-21T19:02:38Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Topology-Guided Multi-Class Cell Context Generation for Digital Pathology [28.43244574309888]
  We introduce several mathematical tools from spatial statistics and topological data analysis. We generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.
  arXiv  Detail & Related papers  (2023-04-05T07:01:34Z)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• Machine learning based lens-free imaging technique for field-portable cytometry [0.0]
  The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types. The model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample.
  arXiv  Detail & Related papers  (2022-03-02T07:09:29Z)
• Interpretable Single-Cell Set Classification with Kernel Mean Embeddings [14.686560033030101]
  Kernel Mean Embedding encodes the cellular landscape of each profiled biological sample. We train a simple linear classifier and achieve state-of-the-art classification accuracy on 3 flow and mass datasets.
  arXiv  Detail & Related papers  (2022-01-18T21:40:36Z)
• Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology [7.568373895297608]
  A key challenge in the analysis of such images is their size, which can run into the gigapixels. We propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field. Our model learns to generate different tissue types with fine details, while simultaneously learning powerful representations that can be used for different clinical endpoints.
  arXiv  Detail & Related papers  (2021-09-07T19:20:01Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.