Title: MAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classification
Venue: NeurIPS 2025
Accepted: yes
Keywords: multiple instance learning

whole slide image classification

prompt learning

vision-language model

few-shot learning
Abstract: Prompt learning has emerged as a promising paradigm for adapting pre-trained vision-language models (VLMs) to few-shot whole slide image (WSI) classification by aligning visual features with textual representations, thereby reducing annotation cost and enhancing model generalization. Nevertheless, existing methods typically rely on slide-level prompts and fail to capture the subtype-specific phenotypic variations of histological entities (e.g., nuclei, glands) that are critical for cancer diagnosis. To address this gap, we propose Multi-scale Attribute-enhanced Prompt Learning (MAPLE), a hierarchical framework for few-shot WSI classification that jointly integrates multi-scale visual semantics and performs prediction at both the entity and slide levels. Specifically, we first leverage large language models (LLMs) to generate entity-level prompts that can help identify multi-scale histological entities and their phenotypic attributes, as well as slide-level prompts to capture global visual descriptions. Then, an entity-guided cross-attention module is proposed to generate entity-level features, followed by aligning with their corresponding subtype-specific attributes for fine-grained entity-level prediction. To enrich entity representations, we further develop a cross-scale entity graph learning module that can update these representations by capturing their semantic correlations within and across scales.  The refined representations are then aggregated into a slide-level representation and aligned with the corresponding prompts for slide-level prediction. Finally, we combine both entity-level and slide-level outputs to produce the final prediction results. Results on three cancer cohorts confirm the effectiveness of our approach in addressing few-shot pathology diagnosis tasks.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work leverages large language models (LLMs) to generate both entity-level and slide-level prompts, integrating fine-grained and coarse-grained visual and textual information for few-shot whole-slide image classification. The proposed MAPLE method consistently outperforms existing SOTA models in this field. The integration of entity-level knowledge is promising to boost few-shot classification performance, especially in the era of LLMs.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n1. This work introduces entity-level descriptions generated from LLMs to provide fine-grained information for visual-textual alignment.\n2. The performance of the proposed MAPLE model is promising, surpassing existing SOTAs.\n3. Experiments are relatively solid, covering three cancer cohorts from the TCGA dataset.\n\n### Weaknesses:\n1. Visuals in this work (for example, Figure 1) are not precise. For instance, the third paragraph of Introduction mentioned that “TOP [38] introduces instance-level phenotypic prompts to guide patch aggregation into slide-level features, while ViLa-MIL [43] leverages learnable visual prototypes to guide the fusion process of patch features and considers dual-scale visual descriptive text prompt to boost the performance.”, however, Figure 1(a) only illustrates the slide-level similarity computation process, which is too simplified and does not correspond to the texts. What’s more, last paragraph of Introduction mentions “MAPLE … across different scales, as illustrated in Fig. 1”, however, Fig. 1 clearly does not contain multi-scale visualization. \n2. Similar concerns are in the texts as well. For example, the second paragraph of Section 2.2 describes the learning process of few-shot WSI analysis. However, it is way too simplified and only mentions the slide-level similarity computation for logits generation. Most existing works computes the logits more than this, such as ViLa-MIL [1] and FOCUS [2], which also apply patch-level similarity computation.\n3. Lack of certain ablation studies. It is mentioned that PLIP is used as the feature extractor for the visual branch of MAPLE, however, as mentioned in FOCUS [2] (Figure 2 Ablation Study on Foundation Models), PLIP typically performs the worst in few-shot WSI classification. The choice of using PLIP is not well explained. Please refer to the Questions section for more related details.\n\n[1] Shi, Jiangbo, et al. \"ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\n[2] Guo, Zhengrui, et al. \"Focus: Knowledge-enhanced adaptive visual compression for few-shot whole slide image classification.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\nQUESTIONS:\n1. Reliability of using LLMs to generate knowledge-required descriptions for entities. Although it’s common practice to utilize LLMs for prompt or diagnosis description generation, it would be better to demonstrate the reliability of the generated prompts. For example, do they accurately describe the slides’ morphology of a certain cancer? I see that Table 6 provides an ablation of the impact of using different LLMs for prompt generation. One thing I’d like to discuss further is that existing works mostly conduct this kind of ablation, yet sometimes the results are not consistent due to different environments or settings. For example, in FOCUS, they show that Claude demonstrates the strongest performance for prompt generation. This kind of difference leads to difficulty in choosing appropriate LLMs in clinical scenarios. Could the authors share some insights regarding this problem?\n2. As mentioned in the Weaknesses part, could the authors provide more explanation regarding the choice of using PLIP as the feature extractor (although there is a comparison between PLIP and CLIP, CLIP achieves even worse performance)? There have been many powerful pathology foundation models (or feature extractors) in recent years, such as UNI [1], GPFM [2], and Virchow [3]. A major concern here is that existing models might use different feature encoders to extract features, for example, FOCUS uses CONCH [4] as the visual encoder. Thus, this lack of sufficient ablation study on feature encoders could lead to unfair comparisons.\n3. Meanwhile, from Table 1, it could be seen that the results are relatively low compared to ones reported from other works. Still take FOCUS for example, the 16-shot classification results of TCGA-NSCLC are around 0.95 AUC for several models. Yet in this work, the same task under the same few-shot setting only achieves around 0.9 AUC. I believe this also stems from the choice of feature extractor, i.e., PLIP.\n4. Could the authors explain more about the visualization details in section 4.3? Are the patches shown for each entity selected from the ones with the highest attention score? \n5. An additional question is that, if the entity can be precisely identified, then what’s the point of using the global description and WSI-level representation for further integration and analysis?\n\n[1] Chen, Richard J., et al. \"Towards a general-purpose foundation model for computational pathology.\" Nature Medicine 30.3 (2024): 850-862.\n\n[2] Ma, Jiabo, et al. \"Towards a generalizable pathology foundation model via unified knowledge distillation.\" arXiv preprint arXiv:2407.18449 (2024).\n\n[3] Vorontsov, Eugene, et al. \"A foundation model for clinical-grade computational pathology and rare cancers detection.\" Nature medicine 30.10 (2024): 2924-2935.\n\n[4] Lu, Ming Y., et al. \"A visual-language foundation model for computational pathology.\" Nature Medicine 30.3 (2024): 863-874.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFew-shot WSI classifiers often miss fine-grained, subtype-specific histological variations crucial for accurate diagnosis. To remedy this, the authors introduce MAPLE, which uses a frozen language model to generate both entity-level prompts for detailed histological attributes and slide-level prompts for global tissue context. A cross-attention module guided by these prompts, together with a cross-scale entity graph, refines and aligns multi-resolution features before aggregating them for prediction, boosting both accuracy and interpretability.\n\nSTRENGTHS AND WEAKNESSES:\n1. Strengths:\n\n(1) MAPLE consistently outperforms leading MIL and prompt-learning baselines across different few-shot settings on multiple datasets.\n\n(2) By fusing entity-level features from both low and high magnification with slide-level cues, MAPLE surpasses single-scale variants. Ablation studies show that every key module (language-guided instance selection, entity-guided cross-attention, and cross-scale graph learning) contributes positively.\n\n(3) With just less trainable parameters and inference/training times comparable to simpler baselines, MAPLE strikes a practical balance between accuracy and efficiency.\n\n2. Weaknesses:\n\n(1) Prompt quality depends entirely on raw LLM descriptions, which can hallucinate or omit clinically crucial details. The authors note this limitation and suggest future human-in-the-loop refinement.\n\n(2) Without ground-truth entity labels, the paper lacks directly quantification on how well selected patches match textual prompts—relying instead on indirect ablation and qualitative figures.\n\n(3) Although the paper highlights gains from multi-scale integration, it doesn’t benchmark against established multi-scale MIL architectures, leaving it unclear how much improvement stems from language cues versus simply using multiple resolutions.\n\nQUESTIONS:\n1. Prompt reliability seems crucial for this study. How did the authors generate prompts that prevent hallucinations or the omission of clinically critical attributes? It is helpful to provide results with and without expert correction to quantify the performance gains from text guidance. The authors also claim that MAPLE’s hierarchy mirrors diagnostic practice, yet they offer no user study or expert evaluation / references to validate this alignment. \n\n2. Although qualitative examples show that selected patches visually match the LLM-described attributes, there are no region-level ground-truth annotations with heatmap visualizations (or IoU / Dice metrics) to demonstrate slide-level alignment. \n\n3. Both the low-resolution-only and high-resolution-only models perform nearly as well as the multi-scale model, which only marginally outperforms the single-scale variants. How can we assess that each scale provides complementary information rather than redundant features? Furthermore, how can we ensure that 10× magnification captures sufficient cellular morphology detail compared with 20× or 40×?\n\n4. The paper does not compare MAPLE against existing multi-scale MIL methods [1, 2, 3]. Could the authors include these baselines to isolate the benefits of their language-guided enhancements?\n\n[1] DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification\n[2] Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning\n[3] Cross-scale multi-instance learning for pathological image diagnosis\n\nLIMITATIONS:\nMAPLE depends on uncurated LLM prompts, risking hallucinations without expert oversight. It lacks pixel-level annotations for rigorous, quantitative region–prompt validation, relying instead on indirect ablation and qualitative examples.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the limitation of existing few-shot WSI classification methods that lack fine-grained, entity-level prompts. It proposes a Multi-scale Attribute-enhanced Prompt Learning approach, which incorporates both slide-level and entity-level prompts. The method fully leverages the capabilities of large language models (LLMs) throughout the process. Furthermore, an entity graph is constructed to enhance instance features and derive entity-level representations. Finally, the predictions from both the entity-level and slide-level are integrated to produce the final classification result.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. To achieve prompt learning aligned with fine-grained features, this paper designs components such as entity name extraction, hierarchical entity graphs, and an entity-guided cross-attention module—all of which are validated through ablation studies.\n 2. By introducing entity-level prompt learning, the paper enables more interpretable predictions at the entity level.\n\nWeaknesses:\n1. The font size in Figure 1 is too small and difficult to read; it is recommended to enlarge the text for better readability.\n2. In Figure 2, the meaning of the arrows between the entity-level features and the learnable prompts is unclear. It is suggested to add symbols or annotations to clarify their purpose.\n3. How does the use of LLM ensure comprehensive and accurate extraction of entity names? Is there a possibility of incomplete or missing entity names? If the extraction is not sufficiently comprehensive, will it affect model performance, and how is this issue addressed?\n4. In the Region Selection module, is there a high degree of overlap among the selected instances, meaning that most image patches come from the same region? Is this phenomenon beneficial or detrimental to the final model performance?\n\nQUESTIONS:\nplease refer to Weaknesses\n\nLIMITATIONS:\nplease refer to Weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe work presents prompting-based learning for few-shot WSI classification. An LLM is used to form prompts at both the entity level and the slide level, at both low and high resolutions. A cross-attention mechanism is used to extract features for each entity, and cosine similarity is then computed with subtype-specific prompts to generate logits for classification. A Graph Attention Network is employed to account for correlations among various entities. Additionally, slide-level representation is formed in a similar manner, where the LLM is prompted to identify slide-level features. Slide-level logits and entity-level logits are then averaged using a parameter lambda to produce the final logits. The effectiveness of the proposed method is evaluated on three WSI datasets across three few-shot settings.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1) The method is clearly and effectively presented.\n\n2) MAPLE explores the use of scale-specific prompts, which is intuitive and well-motivated.\n\n3) The integration of the Graph Attention Network (GAT) is both interesting and effective.\n\n4) Exhaustive ablation studies are provided for each component of the method.\n\n\nWeaknesses:\n\n1) While the proposed method depends on a VLM-based pathology foundation model, the baselines using traditional MIL approaches such as ABMIL and TransMIL could benefit from stronger image-only foundation models like Virchow 2 or UNI2-h. The study lacks a fair comparison in which MIL methods are trained using these state-of-the-art pathology feature encoders.\n\n2) All experiments are conducted with at most a 16-shot setting, which limits the generalizability of the proposed approach to more realistic scenarios where around fifty to several hundred WSIs per class may be available.\n\n3) The paper lacks any comparison with the ConcepPath method, which is highly relevant as it also decomposes slide-level prompts into concept-level prompts and proposes a guided aggregation strategy. Although ConcepPath was developed under a fully supervised setting, it can be readily adapted to few-shot scenarios as well.\n\nQUESTIONS:\n1) Please include baseline results for ABMIL and TransMIL using state-of-the-art pathology foundation models such as Virchow 2 or UNI2-h in Table 1, evaluated across the three datasets and all three few-shot settings. The PLIP model used in this study is a relatively much weaker feature extractor. \n\n2) Provide results for the 32-shot, 64-shot, and fully supervised (all-label) settings, and compare them with the existing baselines in Table 1. These results will offer a clearer perspective on the generalizability of the proposed method across varying levels of label availability.\n\n3) Include a comparison of the proposed method with ConcepPath across multiple few-shot settings (Table 1) to highlight differences in performance.\n\n4) Provide an ablation study evaluating the effect of using different VLMs, such as CONCH or QuiltNet, on the performance of both the proposed method and baseline methods, at least on one representative dataset.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-08"}