Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation

Haidong Zhu; Jialin Shi; Ji Wu

arxiv: 1907.11835 · v1 · pith:4WYHPDVOnew · submitted 2019-07-27 · 💻 cs.CV · cs.LG· eess.IV

Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation

Haidong Zhu , Jialin Shi , Ji Wu This is my paper

Pith reviewed 2026-05-24 15:18 UTC · model grok-4.3

classification 💻 cs.CV cs.LGeess.IV

keywords noisy labelsimage segmentationlabel quality evaluationdeep learningbiomedical imagesautomatic assessmentoverfitting controlnoisy-labeled training

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The pith

A network can automatically judge the quality of its own noisy training labels and train only on the reliable ones for image segmentation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a Pick-and-Learn approach that lets a deep network assess the relative quality of each label in a noisy training set without any external quality scores supplied. It then selects the better labels to update model parameters while an overfitting control module limits damage from the remaining noisy ones. The method is tested on public biomedical segmentation datasets at varying noise levels and is shown to exceed standard training baselines while keeping accuracy and generalization. A sympathetic reader would care because expert annotations in medical imaging are costly and imperfect, so automatic filtering could let models learn from larger but messier collections. The core mechanism is the network's own evolving predictions serving as the quality signal.

Core claim

The Pick-and-Learn strategy enables a deep neural network to automatically evaluate the relative quality of each training label without explicit quality information and to selectively use high-quality ones to tune parameters, augmented by an overfitting control module that maximizes learning from precise annotations during training.

What carries the argument

The automatic label quality evaluation strategy that scores relative label reliability from the network's own outputs and feeds only the cleaner labels into parameter updates.

If this is right

Training can proceed on datasets whose noise level is unknown in advance without requiring additional expert review.
Segmentation models retain high accuracy and generalization even when label noise increases.
The need for post-collection label cleaning or extra annotations is reduced for biomedical tasks.
The same selection process can be applied during training without changing the underlying network architecture.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach could be tested on non-biomedical segmentation datasets to check whether the quality-evaluation step generalizes beyond medical images.
If the network's quality scores correlate with human judgments on held-out clean data, the method might serve as an automatic data-cleaning preprocessor for other tasks.
Extending the overfitting control to other noise-robust training techniques could produce hybrid pipelines that further improve performance at high noise rates.

Load-bearing premise

The network can reliably and automatically evaluate the relative quality of each label in the training set without any explicit quality information being provided.

What would settle it

Run the method on a dataset where ground-truth clean labels are known; if the automatically selected subset contains more errors than a random or baseline selection, or if final segmentation accuracy falls below standard noisy-label baselines, the claim is falsified.

Figures

Figures reproduced from arXiv: 1907.11835 by Haidong Zhu, Jialin Shi, Ji Wu.

**Figure 1.** Figure 1: Two examples of noisy labels in the segmentation problem. Images in the second row are the clean-annotated ground-truth. The third and fourth columns show two types of noisy labels: dilation and erosion. Correct segmentation boundaries are shown in red. Compared with the solutions for low-quality images, noisy labels are more difficult to deal with if no further annotation for quality is available. Most ap… view at source ↗

**Figure 2.** Figure 2: The end-to-end architecture of our proposed label quality evaluation strategy. The segmentation module is the CNN structure module for generating segmentation. The quality awareness module (QAM) is a CNN structure network taking the concatenation of the image and its labels, marked as Segn in the image, as input, and running parallelly with the segmentation module. To re-weight the samples in the same min… view at source ↗

**Figure 3.** Figure 3: Average class accuracy and loss plots of different noise levels on JSRT. Noise 1 and noise 2 represent 1 ≤ ni ≤ 8 and 5 ≤ ni ≤ 13 respectively. Loss curves belong to models trained on the training set with 50% of labels dilated or eroded 8 to 13 pixels. beginning of the training, the network cannot separate between these two types of data. However, the relative score given to the clean samples gradually go… view at source ↗

**Figure 4.** Figure 4: Relative weights and variances for clean and noisy-labeled data. 4 Conclution In this paper, we have proposed a method to tune the segmentation network on noisy-labeled datasets called label quality evaluation strategy, which consists of three parts: segmentation module, quality awareness module, and overfitting control module. Quality awareness module can evaluate the relative quality of the [PITH_FULL_I… view at source ↗

read the original abstract

Deep learning methods have achieved promising performance in many areas, but they are still struggling with noisy-labeled images during the training process. Considering that the annotation quality indispensably relies on great expertise, the problem is even more crucial in the medical image domain. How to eliminate the disturbance from noisy labels for segmentation tasks without further annotations is still a significant challenge. In this paper, we introduce our label quality evaluation strategy for deep neural networks automatically assessing the quality of each label, which is not explicitly provided, and training on clean-annotated ones. We propose a solution for network automatically evaluating the relative quality of the labels in the training set and using good ones to tune the network parameters. We also design an overfitting control module to let the network maximally learn from the precise annotations during the training process. Experiments on the public biomedical image segmentation dataset have proved the method outperforms baseline methods and retains both high accuracy and good generalization at different noise levels.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper sketches an automatic label quality scorer plus overfitting control for noisy segmentation but supplies zero methods or results, so the claims cannot be checked.

read the letter

The main thing to know is that this work proposes Pick-and-Learn: a way for the network to score relative quality of segmentation labels automatically, train only on the better ones, and use an extra module to limit overfitting to noise. It targets the practical cost of expert annotations in medical imaging. The abstract presents the combination of relative quality evaluation for segmentation plus the control module as the contribution, and it claims experiments on a public biomedical dataset show better performance than baselines plus retained accuracy and generalization across noise levels. That would matter if the evidence held up. What the paper does well is naming a real bottleneck and trying to avoid the need for extra clean labels. The stress-test concern about circular dependence looks like it lands: the quality signal must come from the same network trained on the noisy data, and nothing in the abstract shows an independent anchor or proves the initial ranking is better than random. The abstract also gives no dataset name, no noise model, no metrics, no baselines, and no description of how the scoring or control module is implemented. Without those, the central claim that the network can reliably evaluate label quality on its own stays untestable. This is the kind of idea that could interest people building segmentation models on large but imperfect medical datasets, but only after the method and numbers are shown. Based on what is here, the work does not yet deserve referee time; it needs the actual equations, training procedure, and quantitative tables before a serious review makes sense.

Referee Report

2 major / 0 minor

Summary. The paper proposes the Pick-and-Learn framework for handling noisy labels in image segmentation. It introduces an automatic label quality evaluation strategy that allows the network to assess the relative quality of each training label (without explicit quality information) and an overfitting control module to maximize learning from the selected clean subset. The central claim is that this approach outperforms standard baselines while retaining high accuracy and generalization across noise levels, as demonstrated on a public biomedical image segmentation dataset.

Significance. If the method demonstrably breaks the circular dependence between noisy predictions and quality scoring, the result would be significant for medical image analysis, where expert annotations are costly and label noise is common. The automatic, annotation-free nature of the quality evaluation addresses a practical need. The paper's emphasis on generalization at varying noise levels, if supported by rigorous experiments, would strengthen its contribution over existing noisy-label techniques.

major comments (2)

[Abstract] Abstract: The claim that 'the network can reliably and automatically evaluate the relative quality of each label... without any explicit quality information' is load-bearing for the central contribution, yet the description supplies no mechanism (e.g., loss formulation, initialization strategy, or independent anchor) showing how the initial quality scores avoid being shaped by predictions trained on the same noisy data. This directly engages the skeptic concern that early-stage scoring may be no better than random.
[Abstract] Abstract: The assertion that 'Experiments on the public biomedical image segmentation dataset have proved the method outperforms baseline methods and retains both high accuracy and good generalization at different noise levels' is unsupported by any dataset name, noise model, metric, baseline description, or quantitative result. Without these, the outperformance and generalization claims cannot be evaluated and are load-bearing for the paper's empirical contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the abstract. We agree that both claims require additional supporting detail within the abstract itself to allow readers to evaluate them without consulting the full text. We will revise the abstract accordingly. Point-by-point responses are below.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that 'the network can reliably and automatically evaluate the relative quality of each label... without any explicit quality information' is load-bearing for the central contribution, yet the description supplies no mechanism (e.g., loss formulation, initialization strategy, or independent anchor) showing how the initial quality scores avoid being shaped by predictions trained on the same noisy data. This directly engages the skeptic concern that early-stage scoring may be no better than random.

Authors: We acknowledge that the abstract provides no description of the initialization or update mechanism for the quality scores. The full manuscript describes an iterative Pick-and-Learn process that begins with uniform weighting and progressively refines scores via an overfitting-control module, but this is not summarized in the abstract. We will revise the abstract to include a concise statement of the initialization strategy and the role of the overfitting-control module in breaking the circular dependence. revision: yes
Referee: [Abstract] Abstract: The assertion that 'Experiments on the public biomedical image segmentation dataset have proved the method outperforms baseline methods and retains both high accuracy and good generalization at different noise levels' is unsupported by any dataset name, noise model, metric, baseline description, or quantitative result. Without these, the outperformance and generalization claims cannot be evaluated and are load-bearing for the paper's empirical contribution.

Authors: We agree that the abstract must supply concrete experimental details to substantiate the performance claims. We will revise the abstract to name the specific public biomedical dataset, the noise models and levels tested, the evaluation metrics, the baseline methods, and the key quantitative improvements observed. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation self-contained at abstract level with no equations or self-citation chains provided

full rationale

The abstract and available text describe a label quality evaluation strategy and overfitting control module at a high level without presenting equations, fitted parameters, or derivation steps that reduce to inputs by construction. No self-citations, uniqueness theorems, or ansatzes are quoted that could be load-bearing. The central claim of automatic quality assessment is presented as a novel contribution without reducing to a renamed known result or fitted input called prediction. Per the rules, absence of quotable reductions means the derivation is treated as self-contained; a score of 0 is the appropriate honest non-finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only view yields no identifiable free parameters, axioms, or invented entities. The method description implies some selection criterion for clean labels and a control module, but none are specified or quantified.

pith-pipeline@v0.9.0 · 5696 in / 1056 out tokens · 25444 ms · 2026-05-24T15:18:57.562536+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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