BFHD: Bidirectional Feature Harmonization Decomposition for Heterogeneous Clinical Assessments

Yuanhao Zhuo; Zixi Qin; Ling Qin; Wanqing Li

cs.LGOct 14, 2026

BFHD: Bidirectional Feature Harmonization Decomposition for Heterogeneous Clinical Assessments

Authors: Yuanhao Zhuo, Zixi Qin, Ling Qin, Wanqing Li

Organizations: University of Wollongong, Wollongong, NSW 2522, Australia · The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, China

Abstract

Clinical assessments are often collected using heterogeneous assessment systems across centers and time, leading to records that mix different but related sets of measurements. This motivates harmonization beyond total-score linking. We formulate clinical harmonization at the measurement level as a bidirectional recoverability problem: given paired observations from two assessment systems, the goal is to identify which measurements can be reliably translated in both directions within an application-defined tolerance, while separating non-translatable components. We propose Bidirectional Feature Harmonization Decomposition (BFHD), a feasibility-driven framework that enforces bidirectionally coupled translation and uses feature-wise output gating to produce an explicit decomposition in the original measurement space. Experiments on synthetic data and real clinical assessment pairs show that BFHD achieves broader feasible harmonization coverage and improved subset stability compared to baselines.

Figures & tables

Model	Acc X	Acc Y	$J_{X}$	$J_{Y}$
Ours	$0.880_{\pm 0.024}$	$0.830_{\pm 0.024}$	0.933	0.924
CMLP	$0.855_{\pm 0.015}$	$0.810_{\pm 0.049}$	0.975	0.789
NICE	$0.735_{\pm 0.039}$	$0.725_{\pm 0.040}$	0.837	0.814

Table 1: Synthetic feasibility recovery under ground-truth

\varepsilon

(

s_{gen}

=5). Acc X and Acc Y denote index-level structure recovery accuracy for systems

X

and

Y

, respectively (mean ±std across 10 seeds).

J_{X}

and

J_{Y}

report Jaccard similarity. For baseline models without gating, recoverability is inferred post hoc using a relaxed

2\varepsilon_{\text{gen}}

feasibility criterion. Additional results are provided in Appendix D.1 .

Data	Method	$\#Rec_{A}$	$J_{A}$	$\#A>0.7~$	$\#A>0.5~$	$\#Rec_{B}$	$J_{B}$	$\#B>0.7~$	$\#B>0.5~$
	IRT	11	0.733	4.5	10.5	7	0.785	4	7
CAS(A)	XGB	9	0.710	6	9	7	0.842	5	7
$\leftrightarrow$	FRF	9	0.683	4.5	9	7.5	0.858	6	7.5
SNAP(B)	CMLP	9	0.640	5	9	7	0.764	6	7
	NICE	11	0.867	4	11	9	0.774	5	9
	Ours	11	0.930	4	11	9	0.817	5	9

Table 2: Real-world harmonization results under

\varepsilon_{\text{cutoff}}=0.6

. For each row, A and B correspond to the two assessment systems.

\#\mathrm{Rec}_{A}

\#\mathrm{Rec}_{B}

are the median numbers of recoverable measurements and

J_{A}

J_{B}

are Jaccard stability across 10 seeds.

\#A{>}0.7

\#A{>}0.5

(and analogously for B) count selected measurements whose CCC exceeds 0.7 or 0.5, used as diagnostic measures of feasibility.

Figure 1: $\varepsilon$ -sweep on EMBIC and NeuroBAT. Median number of recoverable measurements ( $\#\mathrm{rec}$ ) as a function of the feasibility threshold $\varepsilon$ . CAS $\leftrightarrow$ SNAP results are reported in Appendix D.2 .

Method	#Ret A	Ret% A	#Ret B	Ret% B
IRT	3	$42.9_{\pm 0.0}$	0	N/A
XGB	6	$85.7_{\pm 0.0}$	0.5	$62.5_{\pm 48.4}$
FRF	5	$56.0_{\pm 8.0}$	0	$0.0_{\pm 0.0}$
CMLP	6	$71.0_{\pm 7.5}$	1	$90.0_{\pm 10.0}$
NICE	6	$77.4_{\pm 9.9}$	2	$49.2_{\pm 10.5}$
Ours	7	$100.0_{\pm 0.0}$	3	$100.0_{\pm 0.0}$

Table 3: Temporal deployment utility under protocol drift on the EMBIC(A)

\leftrightarrow

NeuroBAT(B) assessment pair.

\varepsilon_{\text{cutoff}}=0.6

. #Ret A /#Ret B denote the numbers of training-period recoverable measurements that remain feasible on the held-out test split. Ret% A /Ret% B report the corresponding retention rates. (mean ±std across 10 seeds) Additional results in Appendix D.3 .

Variant	$\#\mathrm{Rec}_{A}$	$J_{A}$	$\#\mathrm{Rec}_{B}$	$J_{B}$
Ours (full)	7	0.910	2	0.689
- TWMLP	7	0.886	1	0.600
- $L_{0}$ penalty	$0^{\dagger}$	N/A	$0^{\dagger}$	N/A
- No gating	7	0.806	2	0.523
- No hinge	$13^{\dagger}$	N/A	$9^{\dagger}$	N/A

Table 4: Ablation study on EMBIC (A)

\leftrightarrow

NeuroBAT (B) under

\varepsilon_{\text{cutoff}}=0.6

. Notation are consistent with Table 2 . Additional results in Appendix D.4 .

\dagger

indicate trivial gating solutions.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Deploy $X\rightarrow\hat{Y}$ (source-side: $X$ )
Method	Acc (Obs)	Acc (Trans)
X-only	$0.909\pm 0.047$	-
CMLP	$0.919\pm 0.024$	$0.881\pm 0.047$
NICE	$0.916\pm 0.023$	$0.897\pm 0.033$
Ours	$0.913\pm 0.023$	$0.901\pm 0.037$
Deploy $Y\rightarrow\hat{X}$ (source-side: $Y$ )

Appendix

Table 5: Downstream prediction under size-matched harmonized subsets. Results are reported as mean

\pm

standard deviation over ten random seeds. Obs denotes using observed measurements in the prediction space, while Trans denotes using translated measurements obtained from the opposite view. Single-view reference (X-only / Y-only) serve as reference result.

$p_{\text{miss}}$	Method	Missing in $X$ ( $X_{\text{miss}}\rightarrow\hat{Y}$ )			Missing in $Y$ ( $Y_{\text{miss}}\rightarrow\hat{X}$ )
		#Ret	Retain	Std	#Ret	Retain	Std
0.1	Ours	$9$	$0.860$	$0.097$	$7$	$0.813$	$0.140$
	NICE	$8.5$	$0.788$	$0.152$	$7$	$0.877$	$0.121$
	CMLP	$8$	$0.815$	$0.196$	$6$	$0.725$	$0.293$
0.2	Ours	$6.5$	$0.513$	$0.158$	$5.5$	$0.634$	$0.112$
	NICE	$6.5$	$0.524$	$0.203$	$6$	$0.632$	$0.168$

Appendix

Table 6: Robustness under MCAR missingness. For each method, #Ret denotes the median number of measurements that remain feasible under missingness. Retain denotes the retention rate relative to the reference feasible set defined at

p=0

(Experiment 2). Results are reported as mean

\pm

standard deviation across random seeds.

Data	Method	Acc X	Acc Y	$J_{X}$	$J_{Y}$	$J_{\mathrm{overall}}$	CCC X	CCC Y
	Ours	$0.915\pm 0.023$	$0.900\pm 0.000$	0.948	1.000	0.974	$0.738\pm 0.014$	$0.754\pm 0.063$
$s_{\mathrm{gen}}$ = 1	CMLP	$0.890\pm 0.037$	$0.900\pm 0.000$	0.896	1.000	0.948	$0.768\pm 0.019$	$0.758\pm 0.014$
	NICE	$0.850\pm 0.000$	$0.805\pm 0.047$	1.000	0.824	0.912	$0.722\pm 0.013$	$0.745\pm 0.013$
	Ours	$0.830\pm 0.056$	$0.755\pm 0.042$	0.783	0.812	0.797	$0.574\pm 0.033$	$0.619\pm 0.049$
$s_{\mathrm{gen}}$ = 2	CMLP	$0.810\pm 0.037$	$0.710\pm 0.030$	0.848	0.722	0.785	$0.612\pm 0.023$	$0.678\pm 0.024$
	NICE	$0.550\pm 0.032$	$0.605\pm 0.035$	0.400	0.544	0.472	$0.668\pm 0.043$	$0.648\pm 0.016$

Appendix

Table 7: Experiment 1 (Synthetic): feasibility structure recovery and stability across different data-generation seeds (

s_{\mathrm{gen}}\in\{1,\dots,5\}

). Acc is index-level recoverability label accuracy (mean

\pm

std over training seeds).

J_{X}

and

J_{Y}

are Jaccard similarities of selected recoverable subsets across training seeds;

J_{\mathrm{overall}}

is the average of

J_{X}

and

J_{Y}

. CCC statistics summarize agreement strength among selected measurement indices (mean ± std).

Figure 2: $\varepsilon$ -sweep on CAS $\leftrightarrow$ SNAP. Median number of recoverable measurements (#Rec) as a function of the feasibility threshold $\varepsilon$ , shown separately for CAS (A) and SNAP (B). Results are aggregated over random seeds.

EMBIC $\rightarrow$ NeuroBAT (target: NeuroBAT)
Direction	Method	#Ret	Ret% (mean $\pm$ std)	CCC $>$ 0.5	CCC $>$ 0.6	CCC $>$ 0.7
FRF		0	$0.0\pm 0.0$	0	0	0
IRT		0	-	0	0	0
XGB		0.5	$62.5\pm 48.4$	0.5	0	0
CMLP		1	$90.0\pm 10.0$	1	1	0
NICE		2	$49.2\pm 10.5$	2	1	0

Appendix

Table 8: Extended results for experiment 3. For each deployment direction (source

\rightarrow

target), #Ret is the median number of retained recoverable measurements in the target space, and Ret% is the corresponding retention rate (mean

\pm

std across random seeds), and is reported in percentage units. CCC

>

0.5/0.6/0.7 report the median number of retained measurements whose concordance correlation coefficient on the test period exceeds the corresponding threshold. “-” denotes not applicable (no recoverable measurement is identified on the training period, hence retention is undefined). All results use

\varepsilon_{\text{cutoff}}=0.6

Variant	$\#\mathrm{Rec}_{A}$	$J_{A}$	$\#\mathrm{Rec}_{B}$	$J_{B}$
Ours (full)	11	0.930	9	0.817
TWMLP backbone	11	0.929	9	0.947
$L_{0}$ penalty	$0^{\dagger}$	-	$0^{\dagger}$	-
No gating	11	0.806	9	0.774
No hinge	$27^{\dagger}$	-	$18^{\dagger}$	-

Appendix

Table 9: Ablation results on CAS (A)

\leftrightarrow

SNAP (B) under

\varepsilon_{\text{cutoff}}=0.6

. #Rec A /#Rec B denote median numbers of recoverable measurements.

J_{A}

J_{B}

denote Jaccard similarity across random seeds. Degenerate variants are marked with “-”.

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IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model

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Retrospective MRI harmonization is limited by poor scalability across modalities and reliance on traveling subject datasets. To address these challenges, we introduce IHF-Harmony, a unified invertible hierarchy flow framework for multi-modality harmonization using unpaired data. By decomposing the translation process into reversible feature transformations, IHF-Harmony guarantees bijective mapping and lossless reconstruction to prevent anatomical distortion. Specifically, an invertible hierarchy flow (IHF) performs hierarchical subtractive coupling to progressively remove artefact-related features, while an artefact-aware normalization (AAN) employs anatomy-fixed feature modulation to accurately transfer target characteristics. Combined with anatomy and artefact consistency loss objectives, IHF-Harmony achieves high-fidelity harmonization that retains source anatomy. Experiments across multiple MRI modalities demonstrate that IHF-Harmony outperforms existing methods in both anatomical fidelity and downstream task performance, facilitating robust harmonization for large-scale multi-site imaging studies. Code is available at https://github.com/Idea89560041/IHF-Harmony.

Abstract

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