Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles

• URL: http://arxiv.org/abs/2310.15952v3
• Date: Fri, 10 Nov 2023 09:52:03 GMT
• Title: Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
• Authors: Xing Shen, Hengguan Huang, Brennan Nichyporuk, Tal Arbel
• Abstract summary: We introduce a novel three-stage approach based on transformers and conditional diffusion models. We show that our method improves upon state-of-the-art methods in terms of robustness and confidence calibration.
• Score: 4.642805070301818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep learning models have achieved remarkable success across a range of medical image analysis tasks, deployment of these models in real clinical contexts requires that they be robust to variability in the acquired images. While many methods apply predefined transformations to augment the training data to enhance test-time robustness, these transformations may not ensure the model's robustness to the diverse variability seen in patient images. In this paper, we introduce a novel three-stage approach based on transformers coupled with conditional diffusion models, with the goal of improving model robustness to the kinds of imaging variability commonly encountered in practice without the need for pre-determined data augmentation strategies. To this end, multiple image encoders first learn hierarchical feature representations to build discriminative latent spaces. Next, a reverse diffusion process, guided by the latent code, acts on an informative prior and proposes prediction candidates in a generative manner. Finally, several prediction candidates are aggregated in a bi-level aggregation protocol to produce the final output. Through extensive experiments on medical imaging benchmark datasets, we show that our method improves upon state-of-the-art methods in terms of robustness and confidence calibration. Additionally, we introduce a strategy to quantify the prediction uncertainty at the instance level, increasing their trustworthiness to clinicians using them in clinical practice.

Related papers

• Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification [0.0]
  Colon cancer detection is crucial for increasing patient survival rates.<n> colonoscopy is dependent on obtaining adequate and high-quality endoscopic images.<n>Few-Shot Learning architecture enables our model to rapidly adapt to unseen fine-grained endoscopic image patterns.<n>Our model demonstrated superior performance, achieving an accuracy of 90.1%, precision of 0.845, recall of 0.942, and an F1 score of 0.891.
  arXiv  Detail & Related papers  (2025-05-30T16:54:51Z)
• Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios [0.0]
  Mammographic screening is an effective method for detecting breast cancer, facilitating early diagnosis.<n>Deep neural networks have been shown effective in some studies, but their tendency to overfit leaves considerable risk for poor generalisation and misdiagnosis.<n>Data augmentation schemes based on unpaired neural style transfer models have been proposed that improve generalisability.<n>We evaluate their performance when trained on image patches parsed from three open access mammography datasets and one non-medical image dataset.
  arXiv  Detail & Related papers  (2025-01-29T11:09:50Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness [56.2479170374811]
  We introduce Fine-Tuning with Confidence-Aware Denoised Image Selection (FT-CADIS) FT-CADIS is inspired by the observation that the confidence of off-the-shelf classifiers can effectively identify hallucinated images during denoised smoothing. It has established the state-of-the-art certified robustness among denoised smoothing methods across all $ell$-adversary radius in various benchmarks.
  arXiv  Detail & Related papers  (2024-11-13T09:13:20Z)
• Evidential time-to-event prediction with calibrated uncertainty quantification [12.446406577462069]
  Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. We propose an evidential regression model specifically designed for time-to-event prediction. We show that our model delivers both accurate and reliable performance, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-12T15:06:04Z)
• 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.<n> LTDiff++ is a multiscale latent diffusion model designed to enhance feature extraction in medical imaging.
  arXiv  Detail & Related papers  (2024-10-05T02:13:57Z)
• Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts [2.309018557701645]
  This paper evaluates whether predictive uncertainty estimation adds robustness to deep learning-based diagnostic decision-making systems. We first investigate three popular methods for improving predictive uncertainty: Monte Carlo dropout, deep ensemble, and few-shot learning on lung adenocarcinoma classification as a primary disease in whole slide images.
  arXiv  Detail & Related papers  (2024-08-15T21:49:43Z)
• StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
  StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
  arXiv  Detail & Related papers  (2024-08-11T01:22:29Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Confidence-aware multi-modality learning for eye disease screening [58.861421804458395]
  We propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information. Experimental results on both public and internal datasets demonstrate that our model excels in robustness.
  arXiv  Detail & Related papers  (2024-05-28T13:27:30Z)
• Assessing Uncertainty Estimation Methods for 3D Image Segmentation under Distribution Shifts [0.36832029288386137]
  This paper explores the feasibility of using cutting-edge Bayesian and non-Bayesian methods to detect distributionally shifted samples. We compare three distinct uncertainty estimation methods, each designed to capture either unimodal or multimodal aspects in the posterior distribution. Our findings demonstrate that methods capable of addressing multimodal characteristics in the posterior distribution, offer more dependable uncertainty estimates.
  arXiv  Detail & Related papers  (2024-02-10T12:23:08Z)
• Inadequacy of common stochastic neural networks for reliable clinical decision support [0.4262974002462632]
  Widespread adoption of AI for medical decision making is still hindered due to ethical and safety-related concerns. Common deep learning approaches, however, have the tendency towards overconfidence under data shift. This study investigates their actual reliability in clinical applications.
  arXiv  Detail & Related papers  (2024-01-24T18:49:30Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets [0.7734726150561088]
  A lack of statistically rigorous uncertainty quantification is a significant factor undermining trust in AI results. Recent developments in distribution-free uncertainty quantification present practical solutions for these issues. We demonstrate a technique for forming ordinal prediction sets that are guaranteed to contain the correct stenosis severity.
  arXiv  Detail & Related papers  (2022-07-05T18:01:20Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Robustness via Uncertainty-aware Cycle Consistency [44.34422859532988]
  Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty. We propose a novel probabilistic method based on Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC)
  arXiv  Detail & Related papers  (2021-10-24T15:33:21Z)
• Uncertainty-aware Generalized Adaptive CycleGAN [44.34422859532988]
  Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner. Existing methods often learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty. We propose a novel probabilistic method called Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC)
  arXiv  Detail & Related papers  (2021-02-23T15:22:35Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

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.