Trust the Unreliability: Inward Backward Dynamic Unreliability Driven Coreset Selection for Medical Image Classification

Yan Liang; Ziyuan Yang; Zhuxin Lei; Mengyu Sun; Yingyu Chen; Yi Zhang

cs.CVMar 18, 2026

Trust the Unreliability: Inward Backward Dynamic Unreliability Driven Coreset Selection for Medical Image Classification

Authors: Yan Liang, Ziyuan Yang, Zhuxin Lei, Mengyu Sun, Yingyu Chen, Yi Zhang

Organizations: Sichuan University, Chengdu 610207, P. R. China

Abstract

Efficiently managing and utilizing large-scale medical imaging datasets with limited resources presents significant challenges. While coreset selection helps reduce computational costs, its effectiveness in medical data remains limited due to inherent complexity, such as large intra-class variation and high inter-class similarity. To address this, we revisit the training process and observe that neural networks consistently produce stable confidence predictions and better remember samples near class centers in training. However, concentrating on these samples may complicate the modeling of decision boundaries. Hence, we argue that the more unreliable samples are, in fact, the more informative in helping build the decision boundary. Based on this, we propose the Dynamic Unreliability-Driven Coreset Selection(DUCS) strategy. Specifically, we introduce an inward-backward unreliability assessment perspective: 1) Inward Self-Awareness: The model introspects its behavior by analyzing the evolution of confidence during training, thereby quantifying uncertainty of each sample. 2) Backward Memory Tracking: The model reflects on its training tracking by tracking the frequency of forgetting samples, thus evaluating its retention ability for each sample. Next, we select unreliable samples that exhibit substantial confidence fluctuations and are repeatedly forgotten during training. This selection process ensures that the chosen samples are near the decision boundary, thereby aiding the model in refining the boundary. Extensive experiments on public medical datasets demonstrate our superior performance compared to state-of-the-art(SOTA) methods, particularly at high compression rates.

Figures & tables

Figure 1: Examples of intra-class variation and inter-class similarity in medical image classification.

Figure 2: The framework of our proposed DUCS . DUCS is built upon a novel inward-backward unreliability assessment perspective. (1) To look inward at the model’s self-awareness , we parameterize the final layer outputs as concentration parameters of a Dirichlet distribution to quantify the epistemic uncertainty. This allows us to compute an epistemic uncertainty score $S_{t}^{i}$ for each sample during training and subsequently calculate its variance $V_{t}^{i}$ over a sliding window of epochs. (2) To look backward at the model’s memory tracking , we calculate the forgetting frequency $F_{t}^{i}$ throughout the training history. Ultimately, the coreset is constructed by selecting samples that exhibit both high variance in epistemic uncertainty and high forgetting frequency.

Panel of Figure 3 — Figure 3: t-SNE visualizations. “ $\CIRCLE$ ” and “ $\blacktriangle$ ” denote top 20% unreliable and reliable samples.

	OrganAMNIST							OrganSMNIST
$\beta$	0.5%	1%	2%	5%	10%	20%	30%	0.5%	1%	2%	5%	10%	20%	30%
Full dataset	98.39 $\pm$ 0.02							91.76 $\pm$ 0.55
Random	75.57	84.68	87.63	93.43	95.68	97.30	98.14	48.48	53.27	58.74	73.10	80.95	85.77	87.64
	$\pm$ 2.26	$\pm$ 2.75	$\pm$ 0.76	$\pm$ 0.65	$\pm$ 0.45	$\pm$ 0.13	$\pm$ 0.13	$\pm$ 5.6	$\pm$ 3.74	$\pm$ 0.76	$\pm$ 1.84	$\pm$ 0.66	$\pm$ 1.14	$\pm$ 0.72
Forgetting [ 24 ]	10.32	14.38	15.58	38.53	75.85	97.22	98.11	4.35	4.12	4.33	22.33	33.15	64.43	81.28
	$\pm$ 0.53	$\pm$ 0.63	$\pm$ 0.47	$\pm$ 2.78	$\pm$ 1.69	$\pm$ 0.38	$\pm$ 0.04	$\pm$ 0.02	$\pm$ 0.21	$\pm$ 0.22	$\pm$ 0.31	$\pm$ 0.60	$\pm$ 1.23	$\pm$ 2.31

Table 1: The Performance of ResNet-18 using various coreset selection methods on the MedMNIST medical datasets. All training runs were repeated three times with different random seeds to calculate the mean accuracy along with the standard deviation. The first and second results in each column are highlighted in red and blue, respectively.

Table 5

	PneumoniaMNIST
$\beta$	10%	30%	50%	70%
Full dataset	97.27
Random	92.77	95.70	95.90	96.48
Forgetting [ 24 ]	55.86	96.48	95.70	95.52
Entropy [ 2 ]	71.48	96.09	95.70	97.07
EL2N [ 15 ]	26.17	94.34	97.07	95.90

Table 4: Performance on the small-size medical imaging dataset PneumoniaMNIST with ResNet-18.

	ResNet-50						LeNet						MobileNet-v2
$\beta$	0.5%	1%	2%	5%	10%	20%	0.5%	1%	2%	5%	10%	20%	0.5%	1%	2%	5%	10%	20%
Forgetting [ 24 ]	4.49	4.24	4.05	4.64	35.79	59.42	7.67	7.67	3.87	4.59	24.76	33.59	4.24	4.24	4.08	4.10	28.66	53.47
Entropy [ 2 ]	20.39	22.02	24.22	26.71	48.29	76.90	16.27	20.71	21.83	21.58	47.56	65.92	20.68	22.50	23.54	24.80	54.15	73.00
EL2N [ 15 ]	7.32	6.40	13.70	15.33	56.64	67.24	79.98	11.91	15.74	14.60	46.92	61.08	18.56	16.97	15.99	20.56	57.99	52.34
AUM [ 16 ]	4.24	4.24	4.24	4.10	22.36	37.16	3.87	4.00	4.28	4.30	13.38	32.08	4.24	4.69	4.77	4.00	21.44	37.55
CCS [ 31 ]	31.97	47.68	44.86	43.90	71.88	78.27	40.01	39.80	51.75	52.15	69.48	71.44	47.39	51.92	58.03	45.65	77.98	81.40

Table 5: Cross-architecture generalization performance. We train ResNet-50, LeNet, and MobileNet-v2 models with coresets of OrganSMNIST selected by scores calculated on training dynamics with ResNet-18.

	ResNet-50						LeNet						MobileNet-v2
$\beta$	0.5%	1%	2%	5%	10%	20%	0.5%	1%	2%	5%	10%	20%	0.5%	1%	2%	5%	10%	20%
Forgetting [ 24 ]	11.00	12.91	12.42	34.52	68.33	96.09	8.75	12.09	13.61	23.93	47.33	83.79	8.79	11.89	14.14	25.08	71.25	95.59
Entropy [ 2 ]	9.77	16.78	15.96	19.06	34.71	72.93	4.70	13.28	10.69	17.45	26.84	40.24	9.84	15.65	18.87	19.26	37.73	69.00
EL2N [ 15 ]	8.70	10.15	11.82	21.68	56.63	94.58	8.75	9.21	12.83	19.11	45.65	84.79	9.88	8.83	13.71	24.05	57.99	93.59
AUM [ 16 ]	13.25	9.97	11.86	30.60	34.96	86.52	9.81	8.75	9.51	20.60	32.72	61.93	9.43	11.52	12.51	20.18	33.15	89.91
CCS [ 31 ]	79.76	87.95	82.53	92.33	95.13	96.70	29.75	52.04	62.73	80.43	75.87	80.76	83.65	88.21	83.64	94.04	95.12	97.60

Table 6: Cross-architecture generalization performance. We train ResNet-50, LeNet, and MobileNet-v2 models with coresets of OrganAMNIST selected by scores calculated on training dynamics with ResNet-18.

$\beta$	5%	10%	20%	30%	Full
Organ3D	87.50	95.62	97.50	98.12	98.44
Nodule3D	78.12	81.88	76.88	83.75	82.81
Fracture3D	48.00	48.75	51.25	52.50	53.91

Table 7: Performance on 3D datasets across different selection ratios.

Panel of Figure 4 — Figure 4: The score variations of two randomly selected samples from OrganSMNIST and OrganAMNIST during the training process. Different colors represent different samples.

Panel of Figure 5 — Figure 5: We validate the effectiveness of evaluating sample importance from the two complementary perspectives on the OrganSMNIST and OrganAMNIST datasets.

	OrganAMNIST							OrganSMNIST
$\beta$	0.5%	1%	2%	5%	10%	20%	30%	0.5%	1%	2%	5%	10%	20%	30%
K=5	87.47	93.10	93.27	96.47	97.97	98.29	98.43	59.26	72.38	75.08	83.03	86.70	89.07	89.91
K=10	87.38	90.99	93.27	96.50	97.66	98.44	98.68	58.28	72.45	77.17	84.47	86.71	89.45	89.90
K=20	85.25	85.87	89.99	95.18	96.89	98.09	98.66	47.68	61.95	70.96	80.46	85.03	88.78	89.97
K=40	79.25	83.65	90.03	94.22	95.86	97.66	98.68	50.16	64.36	69.37	80.75	83.61	88.46	89.66

Table 8: Ablation study on window size

K

on the OrganAMNIST and OrganSMNIST. The best results in each column are in red.

Table 13

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 14 — Figure S1 : Ablation study of the inward-looking and backward-looking perspectives. (a) and (b) denote the performance on different datasets.

$\beta$	2%	5%	10%	Full Dataset	score computing time	selection time
EVA	123.03(3.36%)	203.76(5.56%)	323.92(8.85%)	3660.53	17.00	1.00
DUCS	123.03(3.36%)	203.76(5.56%)	323.92(8.85%)	3660.53	23.09	1.00

Appendix

Table S1: Compute time comparison on PathMNIST.

	PathMNIST					COVID-LDCT
$\beta$	2%	5%	10%	20%	30%	2%	5%	10%	20%	30%
Full dataset	99.38					98.97
EVA	75.19	77.90	82.76	87.53	91.10	54.63	60.32	61.82	71.83	96.57
Moderate-DS	52.81	67.62	74.12	84.79	88.22	66.48	67.85	69.77	77.52	80.47
TDDS	73.40	77.59	82.51	90.19	90.60	59.79	59.63	69.29	89.10	92.05
DUCS (Ours)	82.91	90.02	93.49	96.70	98.46	70.12	75.46	81.91	93.63	95.13

Appendix

Table S2 : Performance comparison on PathMNIST and COVID-LDCT datasets. The best and second-best results are marked in red and blue , respectively.

	PathMNIST					COVID-LDCT
$\beta$	2%	5%	10%	20%	30%	2%	5%	10%	20%	30%
Moderate-DS	54.18	56.36	64.50	68.95	75.92	59.15	60.58	62.98	64.70	68.54
TDDS	54.87	75.88	81.18	84.93	84.30	50.51	51.54	52.64	50.45	53.80
EVA	75.85	77.19	83.38	87.60	91.14	52.16	56.61	60.45	63.95	67.51
DUCS (Ours)	80.06	83.98	90.02	91.52	91.52	57.92	61.82	61.82	69.43	69.98

Appendix

Table S3 : Cross-architecture validation on PathMNIST and COVID-LDCT datasets: ResNet-18

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A Coreset Selection Framework with Ensemble Aggregation for Image Classification

Pedro Rocha Dantas, Lucas Pascotti Valem

Institute of Mathematics and Computer Science (ICMC) University of S˜ao Paulo (USP) S˜ao Carlos – SP – Brazil

Training Data Selection Coreset Selection Image Classification

Open paper AskPDF arXiv

The rapid growth of image data has produced large-scale datasets, raising concerns about the time and memory costs of model training. Selecting representative training subsets, however, remains challenging: individual sample contributions are unclear, and model behavior varies across datasets and runs. We address these challenges with a framework that combines coreset selection with an ensemble aggregation over multiple runs. For coreset selection, we propose SCOre-Stratified Selection (SCOSS), which partitions the training data into intervals based on a chosen score and samples from each interval. The ensemble combines predictions from multiple runs, each performed on an independently sampled training subset. As baselines, we use moderate and random selection, each in original and class-balanced versions. We assess the framework with Simple Graph Convolution (SGC) and Support Vector Machine (SVM) classifiers under different sampling ratios. Experiments show that SCOSS is competitive with baselines, often the best choice for SGC, and enables favorable trade-offs between accuracy and efficiency. On the fine-grained dataset, SGC with SCOSS outperforms SVMs when using fewer labeled samples. The code and supplementary materials are publicly available at http://scoss.lucasvalem.com.

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