Reliability-Preserving Compression for Deployable Medical Image Segmentation

Sagar Kumar; Shailja Sharma; Tirthajit Baruah; Punit Rathore

eess.IVOct 18, 2026

Reliability-Preserving Compression for Deployable Medical Image Segmentation

Authors: Sagar Kumar, Shailja Sharma, Tirthajit Baruah, Punit Rathore

Organizations: Indian Institute of Science, India · Indian Institute of Science (IISc), India

Abstract

Post-training compression of pretrained medical segmentation models commonly uses quantization, pruning, or distillation to reduce model size and computational cost. Existing medical segmentation quantization studies mainly evaluate Dice, model size, memory, and latency, but this is incomplete for safety-critical use because compressed models should also preserve uncertainty signals that indicate unreliable predictions. We study this problem in compressed MRI/CT segmentation and formalize why Dice can remain stable while uncertainty changes. Motivated by this failure mode, we propose score-guided aggregate-validated mixed precision (SGAV-MP), a post-training compression framework that selects layer precisions under reliability constraints. SGAV-MP ranks quantization actions with a compact benefit-risk score and accepts them only after full-model reliability validation on a calibration set. Across five 2D backbones and three 3D benchmarks, SGAV-MP preserves FP32-level Dice and high uncertainty-map agreement, achieving $3.59\times$ -- $7.8\times$ compression in 2D and $4.73\times$ -- $7.82\times$ in 3D. We also report backend-supported TensorRT deployment measurements separately to distinguish runtime execution from reliability-preserving storage compression.

Figures & tables

Dataset	Model	Dice	ECE	Brier	UQ Corr.
PROMISE12	EfficientNet-B0	$0.8892\rightarrow 0.882$	$0.0982\rightarrow 0.112$	$0.196\rightarrow 0.22$	$1.000\rightarrow 0.428$
PROMISE12	ResNet50	$0.886\rightarrow 0.881$	$0.103\rightarrow 0.114$	$0.205\rightarrow 0.229$	$1.000\rightarrow 0.324$
MosMedPlus	ResNet50	$0.785\rightarrow 0.651$	$0.206\rightarrow 0.365$	$0.412\rightarrow 0.730$	$1.000\rightarrow 0.119$

Table 1: Representative cases where INT4 quantization may preserve usable Dice while degrading uncertainty and calibration. UQ Corr. denotes uncertainty-map correlation with the FP32 model.

Figure 1: Compression–uncertainty Pareto analysis on MosMedPlus. INT8/INT4 are fixed points; SGAV-MP varies uncertainty tolerance with full-model validation. SGAV-MP improves compression beyond INT8 while preserving uncertainty correlation, whereas INT4 degrades it. The broken y-axis shows both INT4 and the SGAV-MP frontier.

Model	Method	Dice $\uparrow$	ECE $\downarrow$	Brier $\downarrow$	AURC $\downarrow$	Error AUROC $\uparrow$	UQ Corr. $\uparrow$	Comp. Ratio $\uparrow$
ResNet50	FP32	0.8857	0.1025	0.2052	0.00539	0.5881	1.0000	1.00
	INT8	0.8855	0.1032	0.2067	0.00534	0.5879	0.9441	3.98
	INT4	0.8810	0.1144	0.2293	0.00524	0.5848	0.3240	7.91
	MCKP	0.8857	0.1026	0.2056	0.00540	0.5880	0.9945	3.96
	Hessian Greedy	0.8838	0.1101	0.2206	0.00542	0.5835	0.4547	7.90
	AdaRound	0.8864	0.1027	0.2053	0.00542	0.5869	0.7906	3.88

Table 2: MRI segmentation results on PROMISE12/ResNet50. FP32 is the reference and is not ranked. Best compressed value is bold-underlined and second best is bold. For SGAV-MP, Layers / Build / Peak GPU are

62

2450

s /

19915

MB for ResNet50.

Model	Method	Dice $\uparrow$	ECE $\downarrow$	Brier $\downarrow$	AURC $\downarrow$	Error AUROC $\uparrow$	UQ Corr. $\uparrow$	Comp. Ratio $\uparrow$
ResNet50	FP32	0.7850	0.2064	0.4123	0.00464	0.5829	1.0000	1.00
	INT8	0.7849	0.2059	0.4114	0.00465	0.5846	0.9290	3.98
	INT4	0.6507	0.3653	0.7304	0.00653	0.5458	0.1192	7.91
	MCKP	0.7850	0.2067	0.4128	0.00464	0.5829	0.9936	3.97
	Hessian Greedy	0.7717	0.2137	0.4270	0.00491	0.5784	0.2549	7.89
	AdaRound	0.7835	0.2089	0.4163	0.00463	0.5845	0.7667	3.96

Table 3: CT segmentation results on MosMedPlus/ResNet50. FP32 is the reference and is not ranked. Best compressed value is bold-underlined and second best is bold. For SGAV-MP, Layers / Build / Peak GPU are

62

510.1

s /

4719.9

MB.

Dataset	Precision	Dice $\uparrow$	ECE $\downarrow$	Brier $\downarrow$	AURC $\downarrow$	Err-AUROC $\uparrow$	UC $\uparrow$	CR $\uparrow$
AMOS22	FP32	0.7778	0.0527	0.1755	0.00043	0.8719	1.0000	1.00
	FP16	0.7778	0.0527	0.1755	0.00043	0.8719	1.0000	2.00
	INT8	0.7780	0.0525	0.1750	0.00042	0.8722	0.9997	4.00
	INT4	0.7450	0.0703	0.2109	0.00054	0.8603	0.9121	7.98
	SGAV-MP	0.7717	0.0478	0.1679	0.00053	0.8806	0.9857	7.72
BraTS	FP32	0.7334	0.0866	0.2095	0.00013	0.8723	1.0000	1.00

Table 4: 3D SegResNet results on AMOS22 and BraTS. FP32 is the reference and is not ranked. Best compressed value is bold-underlined and second best is bold. For SGAV-MP, build time / peak GPU are

8875

s /

5981

MB on AMOS22 and

28252.6

s /

3430.9

MB on BraTS.

Variant	$\boldsymbol{\alpha_{D}}$	$\boldsymbol{\alpha_{U}}$	$\boldsymbol{\alpha_{R}}$	$\boldsymbol{\alpha_{C}}$	$\boldsymbol{\lambda_{M}}$	Dice $\uparrow$	ECE $\downarrow$	Brier $\downarrow$	AURC $\downarrow$	Err-AUROC $\uparrow$	U-Corr. $\uparrow$	CR $\uparrow$	LQ	Time(s)	Mem(MB)
Dice	1	0	0	0	1	0.8857	0.1026	0.2055	0.0054	0.5883	0.9608	5.07	32	2444.6	19914.2
Dice only (NC)	1	0	0	0	0	0.8857	0.1026	0.2055	0.0054	0.5883	0.9608	5.07	32	2440.4	19914.2
Dice+UQ+risk	1	1	1	0	1	0.8856	0.1029	0.2061	0.0054	0.5884	0.9531	7.29	62	2601.2	19912.4
UQ	0	1	0	0	1	0.8853	0.1037	0.2077	0.0054	0.5882	0.9545	7.17	63	2455.5	19913.9
UQ only (NC)	0	1	0	0	0	0.8853	0.1037	0.2077	0.0054	0.5882	0.9545	7.17	63	2442.6	19913.9
Risk+ECE	0	0	1	1	1	0.8857	0.1026	0.2055	0.0054	0.5883	0.9608	5.07	32	2443.6	19914.7

Table 5: Ablation study on PROMISE12/ResNet50.

D

U

R

, and

C

denote Dice, uncertainty, AURC, and ECE degradation terms. NC removes the connection term. The ablation reruns policy selection under fixed score-term settings. Best compressed value is bold-underlined and second best is bold.

Model	Dice $\uparrow$	ECE $\downarrow$	Brier $\downarrow$	AURC $\downarrow$	Error AUROC $\uparrow$	UQ Corr. $\uparrow$	Abs. Unc.	Comp. Ratio $\uparrow$	Layers Quant.	Build Time (s)	Peak GPU (MB)
ResNet18	0.779	0.212	0.423	0.004	0.586	0.954	0.014	4.153	29	1261.513	12775.755
MobileNetV2	0.784	0.199	0.398	0.004	0.603	0.942	0.012	4.065	43	2441.863	10874.005
EfficientNet-B0	0.788	0.192	0.383	0.004	0.606	0.952	0.012	5.734	77	3614.041	11168.406
DenseNet121	0.791	0.196	0.392	0.004	0.620	0.968	0.013	4.545	120	5355.454	15615.749
ResNet50	0.785	0.206	0.411	0.005	0.583	0.948	0.011	5.454	61	2537.617	16074.700

Table 6: SGAV-MP is calibrated on PROMISE12 and evaluated on MosMedPlus to assess sensitivity to calibration-domain shift

Method	Dice $\uparrow$	ECE $\downarrow$	Brier $\downarrow$	AURC $\downarrow$	Err-AUROC $\uparrow$	U-Corr. $\uparrow$	Lat.(ms) $\downarrow$	Mem.(MB) $\downarrow$	CR $\uparrow$
FP32	0.7273	0.0658	0.2039	0.0007	0.8828	1.0000	2.186	141.50	1.00
FP16	0.7274	0.0657	0.2036	0.0007	0.8828	1.0000	0.862	70.76	2.00
MedPTQ	0.7270	0.0661	0.2046	0.0007	0.8823	0.9946	0.843	88.95	3.90
SGAV-MP	0.7274	0.0654	0.2033	0.0007	0.8825	0.9972	1.580	136.95	1.58

Table 7: Representative 3D AMOS22 TensorRT deployment measurements using the UNet3D. FP32 is the backend reference and is not ranked. Best compressed value is bold-underlined and second best is bold. Build time / peak GPU are

35.7

s /

152

MB for FP16,

19.7

s /

170

MB for MedPTQ, and

30.7

s /

224

MB for SGAV-MP.

Dataset	Model	Precision	CR $\uparrow$	Lat. $\downarrow$	Con-Mem $\downarrow$	Dice $\uparrow$	ECE $\downarrow$	Brier $\downarrow$	AURC $\downarrow$	Err-AUROC $\uparrow$	UC $\uparrow$	Weights $\downarrow$	Total Mem. $\downarrow$
MosMedPlus	EfficientNet-B0	FP32	1.000	1.331	94.563	0.7882	0.1915	0.3825	0.0041	0.6067	1	35.04	129.60
		SGAV-MP	2.250	0.791	34.718	0.7882	0.1914	0.3823	0.0041	0.6067	0.9833	15.58	50.30
		Uniform INT8	3.812	0.903	37.125	0.7881	0.1917	0.3828	0.0041	0.6065	0.9600	9.20	46.33
PROMISE12	ResNet50	FP32	1.000	0.982	88.438	0.8857	0.1026	0.2055	0.0023	0.5883	1	94.11	182.55
		SGAV-MP	2.527	0.594	37.125	0.8857	0.1026	0.2055	0.0023	0.5883	0.9698	37.25	74.18
		Uniform INT8	4.082	0.659	40.813	0.8857	0.1026	0.2055	0.0023	0.5883	0.9426	23.06	65.81

Table 8: Representative 2D TensorRT deployment results. CR denotes compression ratio, UC denotes uncertainty-map correlation, and Con-Mem denotes TensorRT context memory. FP32 is the reference and is not ranked. Best value is bold-underlined and second best is bold.

Model Compression

Model compression reduces the storage, computation, or latency of machine-learning models while preserving task performance.

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SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

Tianhao Fu, Austin Wang, Charles Chen, Roby Aldave-Garza+1

University of Toronto, Toronto, ON, Canada · Vector Institute, Toronto, ON, Canada · Project Neura, Toronto, ON, Canada+4

Image Segmentation Uncertainty Quantification Selective Prediction Medical Image Segmentation Segmentation Uncertainty Estimation Uncertainty Calibration

Open paper AskPDF arXiv

Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support. Many strong uncertainty methods require repeated inference, while efficient single-forward-pass alternatives often provide weaker failure ranking or rely on restrictive feature-space assumptions. We present

\textbf{SegWithU}

, a post-hoc framework that augments a frozen pretrained segmentation backbone with a lightweight uncertainty head. SegWithU taps intermediate backbone features and models uncertainty as perturbation energy in a compact probe space using rank-1 posterior probes. It produces two voxel-wise uncertainty maps: a calibration-oriented map for probability tempering and a ranking-oriented map for error detection and selective prediction. Across ACDC, BraTS2024, and LiTS, SegWithU is the strongest and most consistent single-forward-pass baseline, achieving AUROC/AURC of

0.9838/2.4885

0.9946/0.2660

, and

0.9925/0.8193

, respectively, while preserving segmentation quality. These results suggest that perturbation-based uncertainty modeling is an effective and practical route to reliability-aware medical segmentation. Source code is available at https://github.com/ProjectNeura/SegWithU.

Abstract

Model Compression

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