Histopathology Image Classification using Deep Manifold Contrastive Learning

• URL: http://arxiv.org/abs/2306.14459v1
• Date: Mon, 26 Jun 2023 07:02:07 GMT
• Title: Histopathology Image Classification using Deep Manifold Contrastive Learning
• Authors: Jing Wei Tan, Won-Ki Jeong
• Abstract summary: We propose a novel extension of contrastive learning that leverages geodesic distance between features as a similarity metric for histopathology whole slide image classification. Results demonstrate that our method outperforms state-of-the-art cosine-distance-based contrastive learning methods.
• Score: 8.590026259176806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly used similarity metric in contrastive learning, is not well suited to represent the distance between two data points, especially on a nonlinear feature manifold. Inspired by manifold learning, we propose a novel extension of contrastive learning that leverages geodesic distance between features as a similarity metric for histopathology whole slide image classification. To reduce the computational overhead in manifold learning, we propose geodesic-distance-based feature clustering for efficient contrastive loss evaluation using prototypes without time-consuming pairwise feature similarity comparison. The efficacy of the proposed method is evaluated on two real-world histopathology image datasets. Results demonstrate that our method outperforms state-of-the-art cosine-distance-based contrastive learning methods.

Related papers

• Explainable Metric Learning for Deflating Data Bias [2.977255700811213]
  We present an explainable metric learning framework, which constructs hierarchical levels of semantic segments of an image for better interpretability. Our approach enables a more human-understandable similarity measurement between two images based on the semantic segments within it.
  arXiv  Detail & Related papers  (2024-07-05T21:07:27Z)
• Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning [42.14439854721613]
  We propose a prototypical network with a Bayesian learning-driven contrastive loss (BLCL) tailored specifically for class-incremental learning scenarios. Our approach dynamically adapts the balance between the cross-entropy and contrastive loss functions with a Bayesian learning technique.
  arXiv  Detail & Related papers  (2024-05-17T19:49:02Z)
• Convolutional autoencoder-based multimodal one-class classification [80.52334952912808]
  One-class classification refers to approaches of learning using data from a single class only. We propose a deep learning one-class classification method suitable for multimodal data.
  arXiv  Detail & Related papers  (2023-09-25T12:31:18Z)
• Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization [9.849498498869258]
  Scribble2Label (S2L) demonstrated that using only a handful of scribbles with self-supervised learning can generate accurate segmentation results without full annotation. In this work, we employ a novel multiscale contrastive regularization term for S2L. The main idea is to extract features from intermediate layers of the neural network for contrastive loss so that structures at various scales can be effectively separated.
  arXiv  Detail & Related papers  (2023-06-25T06:00:33Z)
• Contrastive Bayesian Analysis for Deep Metric Learning [30.21464199249958]
  We develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning.
  arXiv  Detail & Related papers  (2022-10-10T02:24:21Z)
• Adaptive Hierarchical Similarity Metric Learning with Noisy Labels [138.41576366096137]
  We propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, textiti.e., class-wise divergence and sample-wise consistency. Our method achieves state-of-the-art performance compared with current deep metric learning approaches.
  arXiv  Detail & Related papers  (2021-10-29T02:12:18Z)
• Discriminative Attribution from Counterfactuals [64.94009515033984]
  We present a method for neural network interpretability by combining feature attribution with counterfactual explanations. We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner.
  arXiv  Detail & Related papers  (2021-09-28T00:53:34Z)
• Deep Relational Metric Learning [84.95793654872399]
  This paper presents a deep relational metric learning framework for image clustering and retrieval. We learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. Experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results.
  arXiv  Detail & Related papers  (2021-08-23T09:31:18Z)
• Towards Interpretable Deep Metric Learning with Structural Matching [86.16700459215383]
  We present a deep interpretable metric learning (DIML) method for more transparent embedding learning. Our method is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods. We evaluate our method on three major benchmarks of deep metric learning including CUB200-2011, Cars196, and Stanford Online Products.
  arXiv  Detail & Related papers  (2021-08-12T17:59:09Z)
• Instance Similarity Learning for Unsupervised Feature Representation [83.31011038813459]
  We propose an instance similarity learning (ISL) method for unsupervised feature representation. We employ the Generative Adversarial Networks (GAN) to mine the underlying feature manifold. Experiments on image classification demonstrate the superiority of our method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-05T16:42:06Z)
• A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces [7.717214217542406]
  We propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams. This framework is built upon a generative adversarial network (GAN) with a diagram distance loss function to steer the learning process. Our proposed method is directly applicable to various datasets without the need of retraining the model.
  arXiv  Detail & Related papers  (2021-05-25T20:44:28Z)

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.