Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery

• URL: http://arxiv.org/abs/2603.03571v1
• Date: Tue, 03 Mar 2026 22:59:24 GMT
• Title: Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery
• Abstract summary: endoscopic video sequences are often contaminated by smoke, specular reflections, blur, and occlusions.<n>Current MDE models do not output depth confidence, which could be a valuable tool for improving their clinical reliability.<n>We propose a novel confidence-aware MDE framework featuring three significant contributions.
• Score: 8.602185221341648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: Monocular depth estimation (MDE) is vital for scene understanding in minimally invasive surgery (MIS). However, endoscopic video sequences are often contaminated by smoke, specular reflections, blur, and occlusions, limiting the accuracy of MDE models. In addition, current MDE models do not output depth confidence, which could be a valuable tool for improving their clinical reliability. Methods: We propose a novel confidence-aware MDE framework featuring three significant contributions: (i) Calibrated confidence targets: an ensemble of fine-tuned stereo matching models is used to capture disparity variance into pixel-wise confidence probabilities; (ii) Confidence-aware loss: Baseline MDE models are optimized with confidence-aware loss functions, utilizing pixel-wise confidence probabilities such that reliable pixels dominate training; and (iii) Inference-time confidence: a confidence estimation head is proposed with two convolution layers to predict per-pixel confidence at inference, enabling assessment of depth reliability. Results: Comprehensive experimental validation across internal and public datasets demonstrates that our framework improves depth estimation accuracy and can robustly quantify the prediction's confidence. On the internal clinical endoscopic dataset (StereoKP), we improve dense depth estimation accuracy by ~8% as compared to the baseline model. Conclusion: Our confidence-aware framework enables improved accuracy of MDE models in MIS, addressing challenges posed by noise and artifacts in pre-clinical and clinical data, and allows MDE models to provide confidence maps that may be used to improve their reliability for clinical applications.

Related papers

• Towards Trustworthy Depression Estimation via Disentangled Evidential Learning [50.22167852149165]
  We propose EviDep, an evidential learning framework that jointly quantifies depression severity.<n>EviDep enforces strict information integrity to guarantee robust evidence synthesis.<n>It achieves state-of-the-art predictive accuracy and superior uncertainty calibration, delivering a robust fail-safe mechanism for trustworthy clinical screening.
  arXiv  Detail & Related papers  (2026-04-17T13:27:11Z)
• Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation [97.36081721024728]
  We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations.<n>Our benchmark unifies three types of medical data for open-ended diagnostic generation.<n>We present MedConf, an evidence-grounded linguistic self-assessment framework.
  arXiv  Detail & Related papers  (2026-01-22T04:51:39Z)
• Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challenges [12.438306093697]
  Large language models (LLMs) have shown promising performance across various tasks.<n>LLMs' autoregressive decoding process poses significant challenges for efficient deployment on existing AI hardware.
  arXiv  Detail & Related papers  (2025-11-27T14:17:43Z)
• BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents [58.05949210993854]
  We investigate whether search agents have the ability to communicate their own confidence through verbalized confidence scores after long sequences of actions.<n>We propose Test-Time Scaling (TTS) methods that use confidence scores to determine answer quality, encourage the model to try again until reaching a satisfactory confidence level.
  arXiv  Detail & Related papers  (2025-10-27T15:58:51Z)
• Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation [63.49409574310576]
  Large language models (LLMs) exhibit overconfidence, assigning high confidence scores to incorrect predictions.<n>We introduce FineCE, a novel confidence estimation method that delivers accurate, fine-grained confidence scores during text generation.<n>Our code and all baselines used in the paper are available on GitHub.
  arXiv  Detail & Related papers  (2025-08-16T13:29:35Z)
• Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation [1.3980986259786223]
  We introduce an efficient ensemble model combining the advantages of Bayesian Approximation and Deep Ensembles.<n>Areas of high model uncertainty and erroneous predictions are aligned which demonstrates the effectiveness and reliability of the approach.
  arXiv  Detail & Related papers  (2025-03-28T09:39:37Z)
• Quantifying Calibration Error in Neural Networks Through Evidence-Based Theory [1.9832598896178517]
  This paper introduces a novel framework for quantifying the trustworthiness of neural networks by incorporating subjective logic into the evaluation of Expected Error (ECE)<n>We demonstrate the effectiveness of this approach through experiments on MNIST and CIFAR-10 datasets where post-calibration results indicate improved trustworthiness.<n>The proposed framework offers a more interpretable and nuanced assessment of AI models, with potential applications in sensitive domains such as healthcare and autonomous systems.
  arXiv  Detail & Related papers  (2024-10-31T23:54:21Z)
• Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding [55.32861154245772]
  Calib3D is a pioneering effort to benchmark and scrutinize the reliability of 3D scene understanding models.<n>We comprehensively evaluate 28 state-of-the-art models across 10 diverse 3D datasets.<n>We introduce DeptS, a novel depth-aware scaling approach aimed at enhancing 3D model calibration.
  arXiv  Detail & Related papers  (2024-03-25T17:59:59Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring [2.5126058470073263]
  This paper proposes Bayesian inference for deep vision structural health monitoring models. Uncertainty can be quantified using the Monte Carlo dropout sampling. Three independent case studies for cracks, local damage identification, and bridge component detection are investigated.
  arXiv  Detail & Related papers  (2020-04-10T17:54:10Z)

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.