Model Checking in Medical Imaging for Tumor Detection and Segmentation

• URL: http://arxiv.org/abs/2501.02024v2
• Date: Tue, 07 Jan 2025 03:29:43 GMT
• Title: Model Checking in Medical Imaging for Tumor Detection and Segmentation
• Authors: Elhoucine Elfatimi, Lahcen El fatimi,
• Abstract summary: Recent advancements in model checking have demonstrated significant potential across diverse applications. Medical imaging stands out as a critical domain where model checking can be effectively applied.
• Score: 0.0
• License:
• Abstract: Recent advancements in model checking have demonstrated significant potential across diverse applications, particularly in signal and image analysis. Medical imaging stands out as a critical domain where model checking can be effectively applied to design and evaluate robust frameworks. These frameworks facilitate automatic and semi-automatic delineation of regions of interest within images, aiding in accurate segmentation. This paper provides a comprehensive analysis of recent works leveraging spatial logic to develop operators and tools for identifying regions of interest, including tumorous and non-tumorous areas. Additionally, we examine the challenges inherent to spatial model-checking techniques, such as variability in ground truth data and the need for streamlined procedures suitable for routine clinical practice.

Related papers

• Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation [10.776242801237862]
  We propose a novel medical image segmentation method that combines diffusion models and Structure-Preserving Network for structure-aware one-shot image stylization. Our approach effectively mitigates domain shifts by transforming images from various sources into a consistent style while maintaining the location, size, and shape of lesions.
  arXiv  Detail & Related papers  (2024-12-05T16:15:32Z)
• RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models [1.4675465116143782]
  This paper introduces the RIDGE checklist to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work.
  arXiv  Detail & Related papers  (2024-01-16T21:45:08Z)
• Towards Automated Semantic Segmentation in Mammography Images [0.0]
  We propose a deep learning-based framework for the segmentation of the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue on standard-view mammography images.
  arXiv  Detail & Related papers  (2023-07-18T15:04:42Z)
• Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images [74.99415008543276]
  Most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain. We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields. A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images. We evaluate our method on the task of intra-subject-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences.
  arXiv  Detail & Related papers  (2022-11-24T23:45:01Z)
• Analysing the effectiveness of a generative model for semi-supervised medical image segmentation [23.898954721893855]
  State-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. Semi-supervised learning (SSL) attempts to leverage the abundance of unlabelled data to obtain more robust and reliable models. Deep generative models such as the SemanticGAN are truly viable alternatives to tackle challenging medical image segmentation problems.
  arXiv  Detail & Related papers  (2022-11-03T15:19:59Z)
• Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models [67.2867506736665]
  We propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.
  arXiv  Detail & Related papers  (2021-12-27T15:28:01Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input [1.5328185694137677]
  We propose an efficient interactive segmentation network that requires minimum input from the user to accurately annotate different tissue types in the histology image. We show that not only does our proposed method speed up the interactive annotation process, it can also outperform the existing automatic and interactive region segmentation models.
  arXiv  Detail & Related papers  (2021-08-30T16:43:03Z)
• A Deep Discontinuity-Preserving Image Registration Network [73.03885837923599]
  Most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous. We propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR) to obtain better registration performance and realistic deformation fields. We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images.
  arXiv  Detail & Related papers  (2021-07-09T13:35:59Z)
• 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)
• Domain Shift in Computer Vision models for MRI data analysis: An Overview [64.69150970967524]
  Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use. Poor transferability of themodels to data from different sources or acquisition domains is one of the reasons for that.
  arXiv  Detail & Related papers  (2020-10-14T16:34:21Z)

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.