mlsanityData to 14 Oct 2026

Using uncertainty estimation to reduce false positives in liver lesion detection

arXiv 2101.04386 ↗First posted 12 Jan 2021eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network using an SVM classifier trained with features derived from the uncertainty map of the neural network prediction. We demonstrate the effectiveness of this method for the detection of liver lesions on a dataset of abdominal MR images. We find that the use of a dropout rate of 0.5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 19 references that are in the map. First posted on arXiv 12 Jan 2021.

Builds on

15 of 19 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2017On Calibration of Modern Neural Networkscited by 4,960
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  7. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  8. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  9. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 163
  10. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  11. 2018Leveraging Uncertainty Estimates for Predicting Segmentation Qualitycited by 44
  12. 2018Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Samplingcited by 20
3 more
  1. 2018Towards increased trustworthiness of deep learning segmentation methods on cardiac MRIcited by 16
  2. 2017Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detectioncited by 11
  3. 2018Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentationcited by 11

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 1.

5 papers.

  1. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 63
  2. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  3. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  4. 2022Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detectioncited by 3
  5. 2022Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentationcited by 1