Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound

• URL: http://arxiv.org/abs/2207.00496v1
• Date: Fri, 1 Jul 2022 15:32:47 GMT
• Title: Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound
• Abstract summary: The incidence of thyroid cancer has been increasing globally. Finding an optimal way to integrate multi-view information also relies on the experience of clinicians. We propose a personalized diagnostic tool that can customize its decision-making process for different patients.
• Score: 12.86992700452335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis. To address these, we propose a personalized diagnostic tool that can customize its decision-making process for different patients. It consists of a multi-view classification module for feature extraction and a personalized weighting allocation network that generates optimal weighting for different views. It is also equipped with a self-supervised view-aware contrastive loss to further improve the model robustness towards different patient groups. Experimental results show that the proposed framework can better utilize multi-view information and outperform the competing methods.

Related papers

• SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis [71.98702207668346]
  Explainability is increasingly seen as a crucial requirement in AI-based medical diagnosis, particularly in safety-critical clinical decision-making.<n>This paper addresses the problem of self-explainable multimodal diagnosis by formulating it within the information bottleneck (IB) framework.<n>We propose a unified learning paradigm that jointly optimize predictive performance and modality-specific explainability.
  arXiv  Detail & Related papers  (2026-09-04T14:13:24Z)
• Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification [7.268080443895895]
  Existing deep learning methods often exhibit limited robustness and generalisation when deployed across different ultrasound devices or clinical environments.<n>We propose PEMV-thyroid, a Prototype-Enhanced Multi-View learning framework that accounts for data heterogeneity.<n>Experiments on multiple thyroid ultrasound datasets demonstrate that PEMV-thyroid consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-03-30T11:37:26Z)
• Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data [76.89269238957593]
  Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views.<n>We propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification.
  arXiv  Detail & Related papers  (2026-02-02T13:07:52Z)
• MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning [52.231128973251124]
  We compare various strategies for predicting survival at the WSI and patient level. The former treats each WSI as an independent sample, mimicking the strategy adopted in other works. The latter comprises methods to either aggregate the predictions of the several WSIs or automatically identify the most relevant slide.
  arXiv  Detail & Related papers  (2025-03-29T11:14:02Z)
• MvKeTR: Chest CT Report Generation with Multi-View Perception and Knowledge Enhancement [1.4680538148112467]
  Multi-view perception knowledge-enhanced Transformer (MvKeTR) MVPA with view-aware attention effectively synthesizes diagnostic information from multiple anatomical views. Cross-Modal Knowledge Enhancer (CMKE) retrieves the most similar reports based on the query volume.
  arXiv  Detail & Related papers  (2024-11-27T12:58:23Z)
• Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence [83.02106623401885]
  We present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries. It achieves an average area under the receiver operating characteristic curve of 0.927 for disease diagnosis and a dice similarity coefficient of 0.878 for lesion segmentation.
  arXiv  Detail & Related papers  (2024-11-25T13:40:11Z)
• Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images [5.395912799904941]
  variations in CT scanner models and acquisition protocols introduce significant variability in the extracted radiomic features. LTDiff++ is a multiscale latent diffusion model designed to enhance feature extraction in medical imaging.
  arXiv  Detail & Related papers  (2024-10-05T02:13:57Z)
• A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness [25.95298616599799]
  We introduce a unified model for longitudinal multi-modal multi-view prediction with missingness. Our method allows as many timepoints as desired for input, and aims to leverage all available data, regardless of their availability. We conduct extensive experiments on the knee osteoarthritis dataset from the Osteoarthritis Initiative for pain and Kellgren-Lawrence grade prediction at a future timepoint.
  arXiv  Detail & Related papers  (2024-03-18T19:51:55Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images [0.0]
  After diagnosis, the number of nonresponding patients to standard front-line therapy remains significant (30-40%) This work aims to develop a computer-aided approach to identify high-risk patients requiring adapted treatment. We propose a method based on recent graph neural networks that combine imaging information from multiple lesions.
  arXiv  Detail & Related papers  (2023-10-25T08:16:45Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data [8.115713756776119]
  We propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient. We use nuclei-level image feature similarity and content-based deep learning method to search for a personalized group of population with similar pathological characteristics.
  arXiv  Detail & Related papers  (2021-10-26T13:12:52Z)
• Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection [49.14070210387509]
  We propose an Anatomy-aware Graph convolutional Network (AGN) for mammogram mass detection. AGN is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-21T06:48:34Z)
• Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis [28.428216831922228]
  This paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. Our objective learns both modality-invariant features and patient-similarity features. We evaluate our method on two public benchmark datasets for retinal disease diagnosis.
  arXiv  Detail & Related papers  (2020-07-21T19:49:45Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)

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.