Validation of Conformal Prediction in Cervical Atypia Classification

• URL: http://arxiv.org/abs/2505.08845v1
• Date: Tue, 13 May 2025 14:37:58 GMT
• Title: Validation of Conformal Prediction in Cervical Atypia Classification
• Authors: Misgina Tsighe Hagos, Antti Suutala, Dmitrii Bychkov, Hakan Kücükel, Joar von Bahr, Milda Poceviciute, Johan Lundin, Nina Linder, Claes Lundström,
• Abstract summary: deep learning based cervical cancer classification can potentially increase access to screening in low-resource regions.<n>Deep learning models are often overconfident and do not reliably reflect diagnostic uncertainty.<n>Con conformal prediction is a model-agnostic framework for generating prediction sets that contain likely classes for trained deep-learning models.
• Score: 1.8988964758950546
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning based cervical cancer classification can potentially increase access to screening in low-resource regions. However, deep learning models are often overconfident and do not reliably reflect diagnostic uncertainty. Moreover, they are typically optimized to generate maximum-likelihood predictions, which fail to convey uncertainty or ambiguity in their results. Such challenges can be addressed using conformal prediction, a model-agnostic framework for generating prediction sets that contain likely classes for trained deep-learning models. The size of these prediction sets indicates model uncertainty, contracting as model confidence increases. However, existing conformal prediction evaluation primarily focuses on whether the prediction set includes or covers the true class, often overlooking the presence of extraneous classes. We argue that prediction sets should be truthful and valuable to end users, ensuring that the listed likely classes align with human expectations rather than being overly relaxed and including false positives or unlikely classes. In this study, we comprehensively validate conformal prediction sets using expert annotation sets collected from multiple annotators. We evaluate three conformal prediction approaches applied to three deep-learning models trained for cervical atypia classification. Our expert annotation-based analysis reveals that conventional coverage-based evaluations overestimate performance and that current conformal prediction methods often produce prediction sets that are not well aligned with human labels. Additionally, we explore the capabilities of the conformal prediction methods in identifying ambiguous and out-of-distribution data.

Related papers

• Pitfalls of Conformal Predictions for Medical Image Classification [1.2289361708127877]
  Con conformal predictions can provide provable calibration guarantees.<n>Con conformal predictions are unreliable under distributional shifts in input and label variables.<n>In classification settings with a small number of classes, conformal predictions have limited practical value.
  arXiv  Detail & Related papers  (2025-06-22T20:33:38Z)
• Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning [53.42244686183879]
  Conformal prediction provides model-agnostic and distribution-free uncertainty quantification.<n>Yet, conformal prediction is not reliable under poisoning attacks where adversaries manipulate both training and calibration data.<n>We propose reliable prediction sets (RPS): the first efficient method for constructing conformal prediction sets with provable reliability guarantees under poisoning.
  arXiv  Detail & Related papers  (2024-10-13T15:37:11Z)
• Trustworthy Classification through Rank-Based Conformal Prediction Sets [9.559062601251464]
  We propose a novel conformal prediction method that employs a rank-based score function suitable for classification models. Our approach constructs prediction sets that achieve the desired coverage rate while managing their size. Our contributions include a novel conformal prediction method, theoretical analysis, and empirical evaluation.
  arXiv  Detail & Related papers  (2024-07-05T10:43:41Z)
• Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests [0.0]
  Conformal Prediction has emerged as a promising method to pair machine learning models with prediction intervals. We propose a method to estimate the uncertainty of each sample by calculating the variance obtained from a Deep Regression Forest.
  arXiv  Detail & Related papers  (2024-02-21T19:09:53Z)
• Beyond Confidence: Reliable Models Should Also Consider Atypicality [43.012818086415514]
  We investigate the relationship between how atypical(rare) a sample or a class is and the reliability of a model's predictions. We show that predictions for atypical inputs or atypical classes are more overconfident and have lower accuracy. We propose that models should use not only confidence but also atypicality to improve uncertainty quantification and performance.
  arXiv  Detail & Related papers  (2023-05-29T17:37:09Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Predictive Inference with Feature Conformal Prediction [80.77443423828315]
  We propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods.
  arXiv  Detail & Related papers  (2022-10-01T02:57:37Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Loss Estimators Improve Model Generalization [36.520569284970456]
  We propose to train a loss estimator alongside the predictive model, using a contrastive training objective, to directly estimate the prediction uncertainties. We show the impact of loss estimators on model generalization, in terms of both its fidelity on in-distribution data and its ability to detect out of distribution samples or new classes unseen during training.
  arXiv  Detail & Related papers  (2021-03-05T16:35:10Z)
• DEUP: Direct Epistemic Uncertainty Prediction [56.087230230128185]
  Epistemic uncertainty is part of out-of-sample prediction error due to the lack of knowledge of the learner. We propose a principled approach for directly estimating epistemic uncertainty by learning to predict generalization error and subtracting an estimate of aleatoric uncertainty.
  arXiv  Detail & Related papers  (2021-02-16T23:50:35Z)
• Private Prediction Sets [72.75711776601973]
  Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
  arXiv  Detail & Related papers  (2021-02-11T18:59:11Z)

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.