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arxiv: 2604.07674 · v1 · submitted 2026-04-09 · 💻 cs.CV

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Weight Group-wise Post-Training Quantization for Medical Foundation Model

Yineng Chen , Peng Huang , Aozhong Zhang , Hui Guo , Penghang Yin , Shu Hu , Shao Lin , Xin Li
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Tzu-Jen Kao Balakrishnan Prabhakaran MingChing Chang Xin Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:12 UTC · model grok-4.3

classification 💻 cs.CV
keywords post-training quantizationmedical foundation modelsweight reorderingchannel-wise scalinglow-bit compressiondot-product quantizationpermutationmodel deployment
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The pith

A post-training method called Permutation-COMQ quantizes medical foundation models to 2, 4, or 8 bits using only dot products, rounding, and weight reordering.

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

The paper presents a quantization algorithm that compresses large medical imaging models after training is complete, without any retraining or search for hyperparameters. It reorders the weights inside each layer before applying channel-wise scaling, then finishes the compression with straightforward dot-product and rounding steps. This combination is meant to recover accuracy that would otherwise be lost when the model is reduced to very low bit widths. A sympathetic reader would care because the resulting models could run on portable medical devices where full-precision inference is impossible due to memory and speed limits.

Core claim

The central claim is that reordering weights group-wise inside each layer compensates for the accuracy degradation caused by channel-wise scaling, while the rest of the quantization reduces to simple dot-product calculations and rounding; when tested on medical foundation models this procedure produces the highest accuracy among compared methods at 2-bit, 4-bit, and 8-bit widths.

What carries the argument

The Permutation-COMQ procedure, which first permutes weights within layers to mitigate channel-wise scaling loss and then applies dot-product-based rounding to obtain the low-bit representation without backpropagation.

If this is right

  • Medical foundation models become deployable on terminal hardware with only minor accuracy loss.
  • Deployment no longer requires gradient-based fine-tuning or hyperparameter sweeps.
  • Channel structure remains intact after quantization, preserving the original model architecture.
  • The same simple dot-product steps can be applied uniformly across 2-bit, 4-bit, and 8-bit targets.

Where Pith is reading between the lines

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

  • The same reordering idea could be tested on non-medical vision transformers to check whether the accuracy recovery is domain-specific.
  • If the method generalizes, it would lower the compute cost of running diagnostic AI in low-resource clinics.
  • One could measure actual inference latency and memory footprint on representative mobile GPUs to quantify the practical gain beyond accuracy numbers.

Load-bearing premise

Reordering the weights inside each layer fully restores the accuracy lost by independent channel scaling and that this fix works for medical foundation models without any further tuning.

What would settle it

Running the method on a previously unseen medical foundation model and finding that its 2-bit accuracy falls below at least one existing post-training quantizer that does not use reordering.

Figures

Figures reproduced from arXiv: 2604.07674 by Aozhong Zhang, Balakrishnan Prabhakaran, Hui Guo, MingChing Chang, Penghang Yin, Peng Huang, Shao Lin, Shu Hu, Tzu-Jen Kao, Xin Li, Xin Wang, Yineng Chen.

Figure 1
Figure 1. Figure 1: Conceptual illustration of COMQ and Permutation-COMQ [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of magnitude of Simulated Weight. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Relative quantization error for COMQ and permutation [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Conceptual illustration of COMQ and Permutation-COMQ. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

Foundation models have achieved remarkable results in medical image analysis. However, its large network architecture and high computational complexity significantly impact inference speed, limiting its application on terminal medical devices. Quantization, a technique that compresses models into low-bit versions, is a solution to this challenge. In this paper, we propose a post-training quantization algorithm, Permutation-COMQ. It eliminates the need for backpropagation by using simple dot products and rounding operations, thereby removing hyperparameter tuning and simplifying the process. Additionally, we introduce a weight-aware strategy that reorders the weight within each layer to address the accuracy degradation induced by channel-wise scaling during quantization, while preserving channel structure. Experiments demonstrate that our method achieves the best results in 2-bit, 4-bit, and 8-bit quantization.

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.

Referee Report

2 major / 2 minor

Summary. The paper introduces Permutation-COMQ, a post-training quantization algorithm for medical foundation models. It relies on dot products and rounding to avoid backpropagation and hyperparameter tuning. A weight-aware reordering strategy is added to counteract accuracy loss from channel-wise scaling while preserving channel structure. The central claim is that experiments show the method achieves the best results for 2-bit, 4-bit, and 8-bit quantization.

Significance. If the experimental results can be substantiated, the work offers a simple, tuning-free quantization procedure that could aid deployment of large medical foundation models on edge devices. The reordering approach targets a specific quantization artifact in a domain where weight statistics may differ from natural-image models.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments section: The headline claim that Permutation-COMQ achieves the best results in 2/4/8-bit settings is presented without any quantitative tables, baseline comparisons, dataset specifications, or error bars. This leaves the central empirical assertion unverified and load-bearing for the paper's contribution.
  2. [Method] Method section on weight-aware reordering: The assertion that reordering fully compensates for accuracy degradation induced by channel-wise scaling while preserving channel structure is not supported by an ablation that isolates the reordering step on the actual weight tensors of the target medical foundation model. Without this, the generalization claim rests on an untested assumption.
minor comments (2)
  1. [Abstract] The abstract uses 'its large network architecture' where 'the model's' or 'their' would be clearer.
  2. [Method] Notation for the dot-product and rounding procedure should be formalized with equations to allow reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for the constructive comments on our manuscript. These observations help strengthen the empirical support and methodological clarity of Permutation-COMQ. We address each major comment below and will incorporate revisions to the manuscript as indicated.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments section: The headline claim that Permutation-COMQ achieves the best results in 2/4/8-bit settings is presented without any quantitative tables, baseline comparisons, dataset specifications, or error bars. This leaves the central empirical assertion unverified and load-bearing for the paper's contribution.

    Authors: We agree that the abstract would be strengthened by including specific quantitative results. The Experiments section reports comparative performance on medical imaging tasks, but we acknowledge the need for greater explicitness. We will revise the abstract to incorporate key metrics (e.g., accuracy or Dice scores) for the 2-bit, 4-bit, and 8-bit cases. In the Experiments section, we will expand the tables to explicitly list all baseline methods, dataset details (including the medical foundation model datasets used), and error bars computed over multiple runs to fully substantiate the central claim. revision: yes

  2. Referee: [Method] Method section on weight-aware reordering: The assertion that reordering fully compensates for accuracy degradation induced by channel-wise scaling while preserving channel structure is not supported by an ablation that isolates the reordering step on the actual weight tensors of the target medical foundation model. Without this, the generalization claim rests on an untested assumption.

    Authors: We appreciate this point. The weight-aware reordering is designed to mitigate accuracy loss from channel-wise scaling by reordering weights within layers according to their magnitude statistics while keeping the original channel ordering intact for architectural compatibility. To directly support this, we will add a dedicated ablation study to the revised manuscript. The ablation will isolate the reordering step by comparing quantization outcomes (with and without reordering) applied to the actual weight tensors extracted from layers of the target medical foundation model, reporting the resulting accuracy differences to demonstrate the compensation effect. revision: yes

Circularity Check

0 steps flagged

Permutation-COMQ presented as direct algorithmic construction using dot products and rounding; no equations reduce accuracy claims to self-fitted parameters

full rationale

The paper describes Permutation-COMQ as a post-training quantization method relying on simple dot products, rounding operations, and a weight-aware reordering strategy to address channel-wise scaling degradation while preserving channel structure. No derivation equations, predictions, or first-principles results are shown that reduce the claimed best results in 2/4/8-bit quantization to inputs by construction (e.g., fitting a parameter on the same data and renaming it a prediction). The accuracy claims rest on experimental validation rather than any self-definitional or fitted-input reduction. This is a standard algorithmic proposal with empirical results and no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract supplies no explicit free parameters or invented entities; the method is described as hyperparameter-free. Standard quantization assumptions (uniform rounding, channel-wise scaling) are implicit but not enumerated.

pith-pipeline@v0.9.0 · 5465 in / 1042 out tokens · 44039 ms · 2026-05-10T17:12:39.784001+00:00 · methodology

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Reference graph

Works this paper leans on

31 extracted references · 13 canonical work pages · 1 internal anchor

  1. [1]

    Deep se- mantic segmentation of natural and medical images: a re- view.Artificial intelligence review, 54(1):137–178, 2021

    Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Co- hen, Julien Cohen-Adad, and Ghassan Hamarneh. Deep se- mantic segmentation of natural and medical images: a re- view.Artificial intelligence review, 54(1):137–178, 2021. 1

    2021

  2. [2]

    SAM2-Adapter: Evaluating & adapting Seg- ment Anything 2 in downstream tasks: Camouflage, shadow, medical image segmentation, and more.arXiv preprint arXiv:2408.04579, 2024

    Tianrun Chen, Ankang Lu, Lanyun Zhu, Chaotao Ding, Chu- nan Yu, Deyi Ji, Zejian Li, Lingyun Sun, Papa Mao, and Ying Zang. Sam2-adapter: Evaluating & adapting seg- ment anything 2 in downstream tasks: Camouflage, shadow, medical image segmentation, and more.arXiv preprint arXiv:2408.04579, 2024. 2

    work page arXiv 2024

  3. [3]

    CoRRabs/2308.16184 (2023)

    Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Ji- long Chen, Lei Jiang, et al. Sam-med2d.arXiv preprint arXiv:2308.16184, 2023. 2

    work page arXiv 2023

  4. [4]

    Medical image anal- ysis: Progress over two decades and the challenges ahead

    James S Duncan and Nicholas Ayache. Medical image anal- ysis: Progress over two decades and the challenges ahead. IEEE transactions on pattern analysis and machine intelli- gence, 22(1):85–106, 2002. 1

    2002

  5. [5]

    GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

    Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers.arXiv preprint arXiv:2210.17323, 2022. 3

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  6. [6]

    Model compression in practice: Lessons learned from practitioners creating on-device machine learning ex- periences

    Fred Hohman, Mary Beth Kery, Donghao Ren, and Dominik Moritz. Model compression in practice: Lessons learned from practitioners creating on-device machine learning ex- periences. InProceedings of the 2024 CHI conference on human factors in computing systems, pages 1–18, 2024. 2

    2024

  7. [7]

    Umednerf: Uncertainty-aware single view vol- umetric rendering for medical neural radiance fields

    Jing Hu, Qinrui Fan, Shu Hu, Siwei Lyu, Xi Wu, and Xin Wang. Umednerf: Uncertainty-aware single view vol- umetric rendering for medical neural radiance fields. In 2024 IEEE International Symposium on Biomedical Imag- ing (ISBI), pages 1–4. IEEE, 2024. 2

    2024

  8. [8]

    Improving generalization of medical image registra- tion foundation model

    Jing Hu, Kaiwei Yu, Hongjiang Xian, Shu Hu, and Xin Wang. Improving generalization of medical image registra- tion foundation model. In2025 International Joint Confer- ence on Neural Networks (IJCNN), pages 1–8. IEEE, 2025

    2025

  9. [9]

    Robustly optimized deep feature decoupling network for fatty liver diseases detection

    Peng Huang, Shu Hu, Bo Peng, Jiashu Zhang, Xi Wu, and Xin Wang. Robustly optimized deep feature decoupling network for fatty liver diseases detection. InInternational Conference on Medical Image Computing and Computer- Assisted Intervention, pages 68–78. Springer, 2024. 2

    2024

  10. [10]

    Diffusion-empowered autoprompt medsam,

    Peng Huang, Shu Hu, Bo Peng, Jiashu Zhang, Hongtu Zhu, Xi Wu, and Xin Wang. Diffusion-empowered autoprompt medsam.arXiv preprint arXiv:2502.06817, 2025. 1, 2

    work page arXiv 2025

  11. [11]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. InProc. IEEE Int. Conf. Comput. Vis. (ICCV), pages 4015–4026, 2023. 1

    2023

  12. [12]

    Is (selective) round-to-nearest quantization all you need?arXiv preprint arXiv:2505.15909, 2025

    Alex Kogan. Is (selective) round-to-nearest quantization all you need?arXiv preprint arXiv:2505.15909, 2025. 3, 6

    work page arXiv 2025

  13. [13]

    Quantizing deep convolutional networks for efficient inference: A whitepaper

    Raghuraman Krishnamoorthi. Quantizing deep convolu- tional networks for efficient inference: A whitepaper.arXiv preprint arXiv:1806.08342, 2018. 3

    work page Pith review arXiv 2018

  14. [14]

    A compre- hensive study on quantization techniques for large language models

    Jiedong Lang, Zhehao Guo, and Shuyu Huang. A compre- hensive study on quantization techniques for large language models. In2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC), pages 224–231. IEEE, 2024. 2

    2024

  15. [15]

    Awq: Activation-aware weight quantization for on-device llm compression and accelera- tion.Proceedings of machine learning and systems, 6:87– 100, 2024

    Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. Awq: Activation-aware weight quantization for on-device llm compression and accelera- tion.Proceedings of machine learning and systems, 6:87– 100, 2024. 3

    2024

  16. [16]

    Robust covid-19 detection in ct images with clip

    Li Lin, Yamini Sri Krubha, Zhenhuan Yang, Cheng Ren, Thuc Duy Le, Irene Amerini, Xin Wang, and Shu Hu. Robust covid-19 detection in ct images with clip. In2024 IEEE 7th International Conference on Multimedia Information Pro- cessing and Retrieval (MIPR), pages 586–592. IEEE, 2024. 2

    2024

  17. [17]

    Abdomenct-1k: Is abdominal organ segmentation a solved problem?IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6695–6714, 2021

    Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, et al. Abdomenct-1k: Is abdominal organ segmentation a solved problem?IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6695–6714, 2021. 6

    2021

  18. [18]

    Segment anything in medical images.Nat

    Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medical images.Nat. Commun., 15(1):654, 2024. 1, 2

    2024

  19. [19]

    Efficient deep learning: A survey on making deep learning models smaller, faster, and better

    Gaurav Menghani. Efficient deep learning: A survey on making deep learning models smaller, faster, and better. ACM Computing Surveys, 55(12):1–37, 2023. 2

    2023

  20. [20]

    A white paper on neural network quantization

    Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yely- sei Bondarenko, Mart Van Baalen, and Tijmen Blankevoort. A white paper on neural network quantization.arXiv preprint arXiv:2106.08295, 2021. 2, 3

    work page arXiv 2021

  21. [21]

    Easyquant: An efficient data-free quan- tization algorithm for llms

    Hanlin Tang, Yifu Sun, Decheng Wu, Kai Liu, Jianchen Zhu, and Zhanhui Kang. Easyquant: An efficient data-free quan- tization algorithm for llms. InProceedings of the 2023 Con- ference on Empirical Methods in Natural Language Process- ing, pages 9119–9128, 2023. 3

    2023

  22. [22]

    Uu-mamba: uncertainty-aware u- mamba for cardiac image segmentation

    Ting Yu Tsai, Li Lin, Shu Hu, Ming-Ching Chang, Hongtu Zhu, and Xin Wang. Uu-mamba: uncertainty-aware u- mamba for cardiac image segmentation. In2024 IEEE 7th International Conference on Multimedia Information Pro- cessing and Retrieval (MIPR), pages 267–273. IEEE, 2024. 2

    2024

  23. [23]

    Uu-mamba: Uncertainty-aware u-mamba for cardiovascular segmentation,

    Ting Yu Tsai, Li Lin, Shu Hu, Connie W Tsao, Xin Li, Ming- Ching Chang, Hongtu Zhu, and Xin Wang. Uu-mamba: Uncertainty-aware u-mamba for cardiovascular segmenta- tion.arXiv preprint arXiv:2409.14305, 2024

    work page arXiv 2024

  24. [24]

    Neural radiance fields in medical imaging: A survey,

    Xin Wang, Yineng Chen, Shu Hu, Heng Fan, Hongtu Zhu, and Xin Li. Neural radiance fields in medical imaging: A survey.arXiv preprint arXiv:2402.17797, 2024. 2

    work page arXiv 2024

  25. [25]

    U-medsam: Uncertainty-aware medsam 8 for medical image segmentation

    Xin Wang, Xiaoyu Liu, Peng Huang, Pu Huang, Shu Hu, and Hongtu Zhu. U-medsam: Uncertainty-aware medsam 8 for medical image segmentation. InMedical Image Segmen- tation Challenge, pages 206–217. Springer, 2024. 2

    2024

  26. [26]

    Magr: Weight magnitude reduc- tion for enhancing post-training quantization.arXiv preprint arXiv:2406.00800, 2024

    Aozhong Zhang, Naigang Wang, Yanxia Deng, Xin Li, Zi Yang, and Penghang Yin. Magr: Weight magnitude reduc- tion for enhancing post-training quantization.arXiv preprint arXiv:2406.00800, 2024. 2

    work page arXiv 2024

  27. [27]

    Comq: A backpropagation-free algorithm for post-training quantiza- tion.arXiv preprint arXiv:2403.07134, 2024

    Aozhong Zhang, Zi Yang, Naigang Wang, Yingyong Qi, Jack Xin, Xin Li, and Penghang Yin. Comq: A backpropagation-free algorithm for post-training quantiza- tion.arXiv preprint arXiv:2403.07134, 2024. 2, 3, 4, 6

    work page arXiv 2024

  28. [28]

    Mobilesamv2: Faster segment anything to everything.arXiv preprint arXiv:2312.09579, 2023

    Chaoning Zhang, Dongshen Han, Sheng Zheng, Jinwoo Choi, Tae-Ho Kim, and Choong Seon Hong. Mobilesamv2: Faster segment anything to everything.arXiv preprint arXiv:2312.09579, 2023. 2

    work page arXiv 2023

  29. [29]

    Fast segment anything,

    Xu Zhao, Wenchao Ding, Yongqi An, Yinglong Du, Tao Yu, Min Li, Ming Tang, and Jinqiao Wang. Fast segment any- thing.arXiv preprint arXiv:2306.12156, 2023. 2

    work page arXiv 2023

  30. [30]

    Contextual reinforcement learning for unsupervised deformable multimodal medical images regis- tration

    Yang Zheng, Hongjiang Xian, Zhikun Shuai, Jing Hu, Xin Wang, and Shu Hu. Contextual reinforcement learning for unsupervised deformable multimodal medical images regis- tration. In2024 IEEE International Joint Conference on Bio- metrics (IJCB), pages 1–9. IEEE, 2024. 2

    2024

  31. [31]

    Cgd-net: A hybrid end-to- end network with gating decoding for liver tumor segmenta- tion from ct images

    Xiaogang Zhu, Tao Liu, Ziqiu Liu, Ouyang Shaobo, Xin Wang, Shu Hu, and Feng Ding. Cgd-net: A hybrid end-to- end network with gating decoding for liver tumor segmenta- tion from ct images. In2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1–7. IEEE, 2024. 2 9

    2024