LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

Won-Ki Jeong; Yonghan Shin

arxiv: 2606.06864 · v1 · pith:UTTLDREDnew · submitted 2026-06-05 · 💻 cs.CV · cs.LG

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

Yonghan Shin , Won-Ki Jeong This is my paper

Pith reviewed 2026-06-27 22:43 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords multiple instance learningwhole slide image classificationknowledge distillationlow-resolution inferencedigital pathologyefficient MILcross-resolution transfer

0 comments

The pith

LRMIL transfers high-resolution knowledge to low-resolution patch embeddings so that a student MIL model can classify whole slide images accurately while using only low-resolution data at inference time.

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

The paper presents LRMIL as a two-stage distillation method that first aligns low-resolution patch embeddings to high-resolution representations and then trains a low-resolution MIL student under both slide labels and high-resolution teacher signals. This setup is motivated by the observation that standard high-resolution MIL approaches incur heavy preprocessing costs and miss global context available at lower magnifications. Because the final model runs exclusively on low-resolution patches, preprocessing and inference become substantially cheaper. Experiments on multiple WSI benchmarks show the distilled low-resolution model outperforming prior MIL methods. The central claim is therefore that cross-resolution distillation preserves enough diagnostic information to make low-resolution inference both efficient and more accurate than existing high-resolution baselines.

Core claim

LRMIL adopts a two-stage distillation strategy. First, patch-level cross-resolution distillation aligns low-resolution patch embeddings with high-resolution representations. Second, slide-level knowledge distillation trains a low-resolution student MIL model under both slide-level supervision and teacher guidance. At inference time, LRMIL operates exclusively on low-resolution patches, substantially reducing data preprocessing and computational cost while outperforming state-of-the-art MIL methods on multiple WSI benchmarks.

What carries the argument

Two-stage knowledge distillation consisting of patch-level cross-resolution alignment followed by slide-level teacher-student training of the MIL aggregator.

If this is right

Inference cost drops because only low-resolution patches need extraction and encoding.
Global visual cues from lower magnifications become available without sacrificing local detail through the distilled representations.
The same low-resolution student model can be deployed across multiple slides without re-extracting high-resolution data.
Slide-level prediction accuracy improves over prior MIL baselines that rely solely on high-resolution patches.

Where Pith is reading between the lines

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

The method may allow pathology labs to scan and store slides at lower resolution from the outset, changing acquisition protocols.
Similar cross-resolution distillation could be tested on other MIL tasks such as video or document classification where resolution or scale trade-offs exist.
If the patch-level alignment step is removed, the slide-level distillation alone might still suffice; this could be checked by ablating the first stage on the same benchmarks.

Load-bearing premise

High-resolution knowledge can be distilled into low-resolution patch representations without losing the information required for correct slide-level predictions.

What would settle it

Training a low-resolution MIL model from scratch on the same low-resolution patches, without any high-resolution teacher, and showing that its accuracy equals or exceeds LRMIL on the reported benchmarks would falsify the necessity of the distillation step.

Figures

Figures reproduced from arXiv: 2606.06864 by Won-Ki Jeong, Yonghan Shin.

**Figure 1.** Figure 1: Overview of our LRMIL framework. (a) Patch-level cross-resolution distillation. Fine-grained semantic knowledge is distilled to a coarse-level patch encoder. (b) Slidelevel distillation for MIL. An LR-based student MIL model is trained using both baglevel supervision and teacher guidance. – We introduce a novel two-stage knowledge distillation strategy, consisting of patch-level cross-resolution distilla… view at source ↗

**Figure 2.** Figure 2: Visual comparison of attention heatmaps. For histologic classification, we use four public datasets: TCGA-BRCA (IDC vs. ILC), TCGA-NSCLC (LUAD vs. LUSC), TCGA-RCC (KIRP vs. KIRC vs. KICH), and BRACS (7 classes) [1,10]. For molecular classification, we use TCGA-BRCA to predict LumA, LumB, Basal, and Her2. For survival prediction, we use TCGA cohorts (BRCA, LUAD, LUSC, KIRP, and KIRC) and formulate the task … view at source ↗

read the original abstract

Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations. Existing MIL methods typically rely on exhaustive extraction and encoding of high-resolution patches. However, this practice suffers from two critical limitations in real-world clinical settings: it struggles to capture global visual cues at lower magnifications, and incurs substantial computational overhead due to the massive number of high-resolution patches per slide. To address these limitations, we propose an efficient low-resolution multiple instance learning (LRMIL) framework that transfers high-resolution knowledge to low-resolution representations. LRMIL adopts a two-stage distillation strategy. First, patch-level cross-resolution distillation aligns low-resolution patch embeddings with high-resolution representations. Second, slide-level knowledge distillation trains a low-resolution student MIL model under both slide-level supervision and teacher guidance. At inference time, LRMIL operates exclusively on low-resolution patches, substantially reducing data preprocessing and computational cost. Extensive experiments on multiple WSI benchmarks demonstrate that LRMIL consistently outperforms state-of-the-art MIL methods while achieving more efficient inference. These results highlight LRMIL as a practical and scalable solution for WSI analysis in clinical pathology.

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

LRMIL's two-stage distillation lets low-res MIL run at inference with claimed SOTA results, but the abstract gives no numbers so the gains are unverified.

read the letter

The core idea is a two-stage process: patch-level alignment of low-res embeddings to high-res ones, followed by slide-level distillation where a low-res student MIL model learns from both labels and a high-res teacher. At test time it drops the high-res path entirely. This directly tackles the compute load from thousands of high-res patches per WSI slide, which is a practical bottleneck in pathology workflows.

The paper does a clean job framing the problem and laying out the mechanism without obvious internal contradictions. The cross-resolution alignment step followed by bag-level KD is a reasonable way to try preserving instance discriminability while shifting the cost to training only.

The main weakness is that the abstract asserts consistent outperformance on multiple benchmarks and more efficient inference, yet supplies zero quantitative results, no dataset details, no baseline comparisons, and no ablation numbers. Without those, it is impossible to judge whether the transferred knowledge actually retains what the MIL aggregator needs or whether the efficiency comes at an accuracy cost that the claims overlook. The stress-test note is right that nothing in the description is internally inconsistent, but that does not substitute for evidence.

This is the kind of work that would interest people building deployable WSI pipelines who already know the standard MIL baselines. A serious referee should see it to check the experiments, ablations, and whether the low-res student really matches or beats the high-res teacher on the reported metrics. I would send it to review rather than desk-reject, but only because the efficiency angle is worth verifying, not because the current write-up stands on its own.

Referee Report

0 major / 1 minor

Summary. The manuscript proposes the LRMIL framework for whole slide image classification using multiple instance learning. It addresses limitations of high-resolution patch processing by distilling knowledge from high-resolution to low-resolution representations through a two-stage process: patch-level cross-resolution distillation to align embeddings, followed by slide-level knowledge distillation to train a low-resolution student model. At inference, only low-resolution patches are used, claiming superior performance and efficiency over existing MIL methods on WSI benchmarks.

Significance. If the experimental claims hold, this would represent a meaningful advance in computational pathology by mitigating the computational costs of high-resolution WSI processing while preserving discriminative power via distillation, potentially enabling more scalable clinical deployment.

minor comments (1)

[Abstract] Abstract: the claim that LRMIL 'consistently outperforms state-of-the-art MIL methods' on 'multiple WSI benchmarks' is stated without any quantitative metrics, table references, or result highlights. Adding 1-2 key performance numbers (e.g., AUC deltas and inference-time reductions) would make the central claim immediately verifiable from the abstract.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their constructive summary of our work and for recommending minor revision. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces LRMIL as an independent two-stage distillation framework (patch-level cross-resolution alignment followed by slide-level KD) that is trained and evaluated on external WSI benchmarks. No derivation chain reduces a claimed prediction or uniqueness result to a fitted parameter or self-citation by construction; the central performance claims rest on empirical comparisons rather than internal redefinitions or ansatzes imported from the authors' prior work. The method description contains no self-definitional equations, no renaming of known results as novel organization, and no load-bearing uniqueness theorems justified solely by overlapping citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; no specific free parameters, additional axioms, or invented entities are detailed.

axioms (1)

domain assumption Multiple instance learning can be applied to whole slide images with slide-level labels only.
This is the foundational assumption of the MIL paradigm in the paper.

pith-pipeline@v0.9.1-grok · 5751 in / 1111 out tokens · 33306 ms · 2026-06-27T22:43:26.415660+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Brancati, N., Anniciello, A.M., Pati, P., Riccio, D., Scognamiglio, G., Jaume, G., De Pietro, G., Di Bonito, M., Foncubierta, A., Botti, G., et al.: Bracs: A dataset for breast carcinoma subtyping in h&e histology images. Database2022, baac093 (2022)

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Advances in neural information pro- cessing systems30(2017)

Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. Advances in neural information pro- cessing systems30(2017)

2017

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In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition

Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition. pp. 5008–5017 (2021)

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Dong, J., Jiang, J., Jiang, K., Li, J., Zhang, Y.: Fast and accurate gigapixel patho- logical image classification with hierarchical distillation multi-instance learning. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 30818–30828 (2025)

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Filiot, A., Dop, N., Tchita, O., Riou, A., Dubois, R., Peeters, T., Valter, D., Scal- bert, M., Saillard, C., Robin, G., et al.: Distilling foundation models for robust and efficient models in digital pathology. In: International Conference on Medi- cal Image Computing and Computer-Assisted Intervention. pp. 162–172. Springer (2025) 10 Shin et al

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In: Proceedings of the 33rd ACM International Conference on Multimedia

Guo, H., Zhang, Q., Gao, Z., Yang, S., Peng, S., Tao, X., Yu, T., Wang, Y., Li, Q.: Efficient multi-slide visual-language feature fusion for placental disease classifica- tion. In: Proceedings of the 33rd ACM International Conference on Multimedia. pp. 8018–8027 (2025)

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Guo, Z., Xiong, C., Ma, J., Sun, Q., Feng, L., Wang, J., Chen, H.: Focus: Knowledge-enhanced adaptive visual compression for few-shot whole slide image classification. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 15590–15600 (2025)

2025

[8] [8]

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)

work page internal anchor Pith review Pith/arXiv arXiv 2015

[9] [9]

In: International conference on machine learning

Ilse,M.,Tomczak,J.,Welling,M.:Attention-baseddeepmultipleinstancelearning. In: International conference on machine learning. pp. 2127–2136. PMLR (2018)

2018

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Patterns5(3) (2024)

Kefeli, J., Tatonetti, N.: Tcga-reports: A machine-readable pathology report re- source for benchmarking text-based AI models. Patterns5(3) (2024)

2024

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In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition

Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 14318–14328 (2021)

2021

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Lin, S., Xie, H., Wang, B., Yu, K., Chang, X., Liang, X., Wang, G.: Knowledge distillation via the target-aware transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10915–10924 (2022)

2022

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Nature Medicine30(3), 863–874 (2024)

Lu, M.Y., Chen, B., Williamson, D.F., Chen, R.J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L.P., Gerber, G., et al.: A visual-language foundation model for computational pathology. Nature Medicine30(3), 863–874 (2024)

2024

[14] [14]

Nature biomedical engineering5(6), 555–570 (2021)

Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nature biomedical engineering5(6), 555–570 (2021)

2021

[15] [15]

Advances in neural information processing systems34, 2136–2147 (2021)

Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: Transmil: Trans- former based correlated multiple instance learning for whole slide image classifica- tion. Advances in neural information processing systems34, 2136–2147 (2021)

2021

[16] [16]

In: European Conference on Computer Vision

Thandiackal, K., Chen, B., Pati, P., Jaume, G., Williamson, D.F., Gabrani, M., Goksel, O.: Differentiable zooming for multiple instance learning on whole-slide im- ages. In: European Conference on Computer Vision. pp. 699–715. Springer (2022)

2022

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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Yang, Z., Li, Z., Zeng, A., Li, Z., Yuan, C., Li, Y.: Vitkd: Feature-based knowledge distillation for vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1379–1388 (2024)

2024

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In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Yang, Z., Zeng, A., Li, Z., Zhang, T., Yuan, C., Li, Y.: From knowledge distillation to self-knowledge distillation: A unified approach with normalized loss and cus- tomized soft labels. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 17185–17194 (2023)

2023

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In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S.E., Zheng, Y.: Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathol- ogy whole slide image classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 18802–18812 (2022)

2022

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Virchow2: Scaling self- supervised mixed magnification models in pathology

Zimmermann, E., Vorontsov, E., Viret, J., Casson, A., Zelechowski, M., Shaikovski, G., Tenenholtz, N., Hall, J., Klimstra, D., Yousfi, R., et al.: Virchow2: Scal- ing self-supervised mixed magnification models in pathology. arXiv preprint arXiv:2408.00738 (2024)

work page arXiv 2024