Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection

• URL: http://arxiv.org/abs/2403.02307v2
• Date: Thu, 25 Jul 2024 04:40:04 GMT
• Title: Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection
• Authors: P. Bilha Githinji, Xi Yuan, Zhenglin Chen, Ijaz Gul, Dingqi Shang, Wen Liang, Jianming Deng, Dan Zeng, Dongmei yu, Chenggang Yan, Peiwu Qin,
• Abstract summary: This study introduces the notion of a population-level context for pathology detection and employs a graph theoretic approach to model and incorporate it into the latent code of an autoencoder. PopuSense seeks to capture additional intra-group variations inherent in biomedical data that a local or global context of the convolutional model might miss or smooth out.
• Score: 17.87825422578005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models. Moreover, these models exhibit a bias for contrast-based images, with diminished performance on texture-based medical images. This study introduces the notion of a population-level context for pathology detection and employs a graph theoretic approach to model and incorporate it into the latent code of an autoencoder via a refinement module we term PopuSense. PopuSense seeks to capture additional intra-group variations inherent in biomedical data that a local or global context of the convolutional model might miss or smooth out. Proof-of-concept experiments on contrast-based and texture-based images, with minimal adaptation, encounter the existing preference for intensity-based input. Nevertheless, PopuSense demonstrates improved separability in contrast-based images, presenting an additional avenue for refining representations learned by a model.

Related papers

• Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration [50.62725807357586]
  This article presents a general Bayesian learning framework for multi-modal groupwise image registration. We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables. Experiments were conducted to validate the proposed framework, including four different datasets from cardiac, brain, and abdominal medical images.
  arXiv  Detail & Related papers  (2024-01-04T08:46:39Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion. This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Deep Variational Lesion-Deficit Mapping [0.3914676152740142]
  We introduce a comprehensive framework for lesion-deficit model comparison. We show that our model outperforms established methods by a substantial margin across all simulation scenarios. Our analysis justifies the widespread adoption of this approach.
  arXiv  Detail & Related papers  (2023-05-27T13:49:35Z)
• CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification [6.9596321268519326]
  In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. We propose the CellMix framework, which employs a novel distribution-oriented in-place shuffle approach. Our experiments in pathology image classification tasks demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets.
  arXiv  Detail & Related papers  (2023-01-27T03:17:35Z)
• Automated segmentation and morphological characterization of placental histology images based on a single labeled image [0.0]
  A novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. A diversified artificial dataset of images is generated that can be used for training deep learning segmentation models.
  arXiv  Detail & Related papers  (2022-10-07T14:00:10Z)
• Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation [8.772764547425291]
  CISFA builds on image domain translation and unsupervised feature adaptation for cross-modality biomedical image segmentation. We use a one-sided generative model and add a weighted patch-wise contrastive loss between sampled patches of the input image and the corresponding synthetic image. We evaluate our methods on segmentation tasks containing CT and MRI images for abdominal cavities and whole hearts.
  arXiv  Detail & Related papers  (2022-07-27T01:49:26Z)
• Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models [16.218680291606628]
  Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders. We propose to train simulator networks that can warp a given image to inject or remove patterns of the disease. We apply our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of the Alzheimer's disease and alcohol use disorder.
  arXiv  Detail & Related papers  (2021-02-16T15:57:37Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.