Interpretable point cloud classification using multiple instance learning

Authors: Matt De Vries, Reed Naidoo, Olga Fourkioti, Chris Bakal
Primary area: interpretability and explainable AI
Keywords: Multiple instance learning, point cloud, interpretable

Abstract:
3D image analysis is crucial in fields such as autonomous driving and biomedical research. However, existing 3D point cloud classification models lack interpretability, limiting trust and usability in safety-critical applications. To address this, we propose PointMIL, an inherently locally interpretable point cloud classifier using Multiple Instance Learning (MIL). PointMIL offers local interpretability, providing fine-grained point-specific explanations to point-based models without the need for \textit{post-hoc} methods, addressing the limitations of global or imprecise interpretability approaches. We applied PointMIL to four popular point cloud classifiers, PointNet, DGCNN, CurveNet, PointMLP, and PointNeXt, and proposed a transformer-based backbone to extract high-quality point-specific features. PointMIL made these models inherently interpretable while increasing predictive performance on standard benchmarks (ModelNet40, ShapeNetPart) and achieving state-of-the-art mACC ($97.3\%$) and F1 ($97.5\%$) on the IntrA biomedical data set, and another dataset of biological cells. To our knowledge, this is the first work to apply MIL to interpretable point cloud classification.

Decision: reject
Reviewer scores: 6, 5, 6, 5

Reviews and discussion:
<thread>
creation date:
1729683019153

thread_type:
official_review

<thread_object>
creator: reviewer_cz3u

<content>
summary:
This paper introduces multiple instance learning to provide local interpretability for point cloud classification models. Experiments are conducted on several popular point cloud classification models and classification benchmarks, demonstrating the effectiveness of the proposed method.

strengths:
This paper is the first to introduce multiple instance learning for interpretable learning in the 3D domain. It can be integrated as a simple module into existing point cloud classification models, effectively enhancing classification performance while also improving interpretability.

weaknesses:
1.The point cloud classification models applied in PointMIL appear to be relatively outdated, as more advanced models such as PointNeXt and PointMLP, as well as more classic models like PointNet++, have not been included in the experiments.

2.The practicality of PointMIL in real-world applications remains questionable, as it lacks experiments on real datasets such as ScanObjectNN, or robustness tests against noise, rotation, and other transformations.

3.There are some minor errors in the details: $\textbf{(a)}$. it seems that $\hat{y}$ in Equation 8 is not defined. $\textbf{(b)}$. there is an error in Table 4 of the appendix

questions:
1.Is the performance comparison on classification tasks fair? If I understand correctly, PointMIL eliminates downsampling to achieve point-level predictions, whereas the other methods compared, such as PointNet++, still employ downsampling. The authors should consider the impact of downsampling on performance.

2.I recommend that the authors include experimental results for PointMIL on ScanObjectNN and test the interpretability on more advanced point cloud models such as PointNeXt and PointMLP.

3.Is PointMIL also applicable to scene-level tasks, such as scene segmentation that requires generating point-level predictions? This could effectively demonstrate the applicability of PointMIL.


4. I hope the authors can provide a detailed explanation of the results, for example, why $\textbf{Additive}$ and $\textbf{Conjunctive}$ achieve better results compared to other MIL pooling methods in Figure 2. Such an exploration would be beneficial for understanding how to choose MIL pooling methods under different circumstances.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730191844496

thread_type:
official_review

<thread_object>
creator: reviewer_yux5

<content>
summary:
This paper proposes a method for generating point-level importance in point cloud classification, aiming to interpret the contribution of each point to the classification outcome. A modified Transformer is introduced to extract point-level features, with MIL pooling applied to determine each point's importance. The paper presents a strong motivation and is well-organized.

strengths:
The motivation is innovative, focusing on the interpretability of point cloud models and aiming to address the issue of poor interpretability in existing models.

weaknesses:
1. The authors aim to propose a general interpretability method; however, they add a Transformer-based feature extractor to existing models as an additional modification to the backbone network. This modification can be seen as an alteration to the backbone, which may impact the original performance of the point cloud network. Consequently, conducting interpretability analysis on this modified version may not align with the original motivation.

2. The novelty is limited: the authors' contributions primarily include (1) the feature extractor and (2) MIL pooling. However, the core iead of the feature extractor is merely a straightforward combination of existing Transformer and DGCNN approaches, lacking significant originality. In the MIL pooling, existing ideas are directly applied without any improvements tailored to the inherent characteristics of point cloud data. These factors restrict the paper's novelty, as it largely reflects a simple combination and adaptation of existing methods.

3. The performance improvements are likely attributed mainly to the additional feature extraction network.

questions:
Although the authors provide visualizations of points deemed important for classification in the figures, these points are derived from the final pooling output. Why are these highlighted red points considered more important—just because they have higher importance scores? The current conclusions remain insufficiently convincing. Are there alternative methods that could offer supplementary validation? One possible approach could involve removing the red points and observing a significant accuracy drop, while removing the gray points leads to no substantial accuracy decrease. However, this is an intuitive idea and may not hold in actual experiments.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730343208075

thread_type:
official_review

<thread_object>
creator: reviewer_1ybp

<content>
summary:
This paper introduces PointMIL, the first framework to apply MIL to point cloud classification. PointMIL provides fine-grained point-specific interpretability without post-hoc techniques.

strengths:
1.	This paper is well-written, and the organization is great.
2.	The motivation is clear enough.

weaknesses:
1.	The backbones and compared methods are limited. There are lots of methods that have been proposed for point cloud classification and segmentation. It is recommended to compare with them, including Point-MAE and PointTransformer V3.
2.	“Group features through k-nearest neighbours”, “Learned relative positional encoding”, and “Attention on the augmented features” are also widely used in point cloud processing, including PoinTr, Point-BERT, or DGCNN. It is recommended to move these parts into appendix and focus on your interpretation. 
3.	The logic in L90-L94 is puzzling. “Post-hoc or inherently interpretable” and “local or global approaches” could be organized better.
4.	The citation in L100 should be (Tan & Kotthaus, 2022). The remaining part should also be carefully checked.

questions:
1.	It is claimed that “most local interpretability methods for point cloud classification are post-hoc” and “no one has yet offered an inherently locally interpretable model for point cloud classification” seems conflict.
2.	Local and global features are widely studied in point cloud classification. Why your local approach is effective than others?
3.	MIL pooling has been widely used in 2D images. Please clarify the main difference between these methods. And it is also recommend to exploit the specific design for point cloud.
4.	The main contribution of this paper is the interpretation. However, the discussion about the interpretation is limited and the discussion about the network design has a great portion.
5.	The performance gain on ModelNet40 and ShapeNetPart is marginal. Moreover, more newly proposed backbones should be included as your backbone.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730511921471

thread_type:
official_review

<thread_object>
creator: reviewer_9g12

<content>
summary:
This paper introduces PointMIL, an inherently locally interpretable point cloud classifier using Multiple Instance Learning (MIL). It addresses a gap in existing classification methods, which either employ post-hoc interpretability techniques or focus on global interpretability. PointMIL comprises a feature encoder, implemented using either 3D transformers, DGCNN, or PointNet, to capture per-point features. A MIL pooling layer provides interpretability, with several types of MIL pooling methods explored, including Instance, Attention, Additive, and Conjunctive pooling.  A contextual prior is also injected into attention mechanism to learn local information. Experiments are conducted on medical dataset IntrA and general 3D object dataset ModelNet40.

strengths:
1. The paper is well-written and easy to follow.
2. It claims to be the first work to achieve locally interpretable point cloud classification on a per-point basis.
3. The visual results are compelling, highlighting important regions for classification. Quantitative results on multiple classification benchmarks, such as IntrA and ModelNet40, and part segmentation benchmarks, including IntrA and ShapeNetPart, demonstrate performance improvements over baselines.

weaknesses:
The technical contribution appears somewhat limited, as it primarily involves combining various typical point cloud encoders with existing MIL pooling methods for point cloud classification. I would have expected a deeper exploration of this combination. For example, were any modifications made to the MIL pooling methods to better adapt them to point cloud data? What specific challenges were encountered and addressed when applying MIL to this domain? Providing more insights into these aspects could help strengthen the contribution and highlight the distinctiveness of the approach.

Furthermore, in Tables 4, 5, and 6 in Appendix A.1, the paper presents varying performance across different MIL pooling methods. However, there is a lack of analysis explaining why certain methods perform better than others, and how these insights could guide the selection or adaptation of pooling methods in future work. Simply presenting the empirical results without such analysis misses an opportunity to make the work more insightful and inspiring.

The backbones used for evaluation are somewhat limited, as more recent and widely adopted models, such as Point Transformer (ICCV 2021) and sparse convolutional architectures, are not included. Evaluating PointMIL with these modern architectures would provide a more comprehensive comparison. I would appreciate it if the authors could discuss how PointMIL might be adapted to work with these newer backbones.  Additionally, a side-by-side comparison of the interpretability outputs for the same representative examples using different backbones would be valuable to determine if they highlight the same local regions of interest.

questions:
1. In Lines 195-197, the paper claims to use five MIL pooling methods, but only four are listed: Instance, Attention, Additive, and Conjunctive. Could the authors please clarify whether a fifth pooling method was intended and omitted, or if this is a typo that should be corrected?
2.  In Table 2,  PointNet is reported to achieve 86.0 mACC, whereas the original paper cites 86.2 mACC. Could the authors please verify this discrepancy and explain? I wonder if this difference is due to variations in the implementation, experimental setup, or evaluation criteria.
3. Could the authors provide visual results for segmentation similar to Figures 2-5? Specifically, it would be interesting to include side-by-side comparisons of classification and segmentation interpretability outputs for a the same representative examples.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732538167171

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General repsponse to all reviewers

comment:
We sincerely thank all reviewers for their thoughtful feedback and positive comments on our paper's novelty, innovativeness, organisation, and writing. We deeply appreciate the time and effort invested in providing detailed and constructive critiques. Each comment has contributed significantly to improving the quality and clarity of our work.

We have now addressed all weaknesses and questions raised by the reviewers. Changes in the manuscript are highlighted in blue. Below, we summarise the major updates:

### 1. **Additional backbone:**

In response to the unanimous recommendation to evaluate PointMIL on a more advanced backbone, we conducted experiments using  **CurveNet** [1]. This backbone originally employs sampling and grouping, which results in a loss of point-level features at the classification stage. We adapted CurveNet by removing sampling and grouping, allowing it to retain per-point features required for PointMIL’s interpretability framework. We therefore show how PointMIL can be used on a slightly adapted version of these architectures where the sampling and grouping is removed, allowing point-level features at the point of classification. 
* The PointMIL version of CurveNet improved accuracy across all datasets and importantly provided inherent interpretability. Please see Tables 1, and 2, and Figure 5. 
* Similar adaptations can be made to PointMLP, PointNeXt, Point Transformer, PointNet++, and any other backbone that uses sampling and grouping. 

### 2. **Increased focus on interpretability:**

To emphasise our core contribution of inherent interpretability, we made the following enhancements:
* Added **perturbation curves** to all interpretability figures, illustrating how removing points deemed most important by the model leads to a significant drop in prediction confidence.
* Expanded discussions on the differences among MIL pooling methods and backbones, providing practical guidelines for selecting the appropriate methods based on task requirements.
* Included qualitative and quanttative examples demonstrating how interpretations vary across backbones for the same input, further validating the reliability of PointMIL’s interpretability outputs.


### 3. **Highlighting the Adaptation of MIL to Point Clouds:**

A major technical contribution of our paper is the adaptation of attention-based MIL to point cloud analysis through **contextual attention**. By assuming that points in close proximity should have similar attention weights, contextual attention smooths importance scores and reflects local geometric structures. This mechanism improved both interpretability and classification metrics, as evidenced by our experiments. We have now emphasised this contribution more clearly in the manuscript.

### 4. **Robustness Tests:**
To assess real-world applicability, we evaluated PointMIL’s robustness to noise on the IntrA dataset. PointMIL outperformed baselines like DGCNN, PointMLP, and PointNet under noisy conditions, showing its ability to focus on informative regions while ignoring noisy data.

### 5. **Real-World Applicability:**
PointMIL was evaluated on real-world biomedical datasets (IntrA, RBC), addressing practical use cases. While ScanObjectNN was beyond the scope due to time constraints, our robustness analysis and results on diverse datasets substantiate its applicability.


### 6. **Clarifications and Corrections:**
* Resolved typos and ambiguities (e.g., fifth pooling method clarification, corrected citations).
* Expanded explanations in the manuscript to address questions about downsampling impacts, MIL pooling advantages, and applicability to scene-level tasks.
* Clarified the distinction between post-hoc and inherent interpretability, outlining how PointMIL fills the gap in inherently interpretable local methods.

These improvements underscore PointMIL's novelty as the first inherently interpretable local framework for point cloud classification. We have now evaluated a total of 16 models on four datasets. 

We hope that these revisions address the concerns and enhance the overall quality of our paper. Thank you again for the time and effort in reviewing our work. 


[1] Tiange Xiang, et al. Walk in the cloud: Learning curves for point clouds shape analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 915–924, October 2021.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732659870282

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Second revision (Extended PointMIL to PointNeXt)

comment:
We thank the reviewers for their continued response. To further address the reviewers concerns, we have made a second revision, summarised below:

### **1. Second additional backbone (PointNeXt, 2022):**

After originally extending PointMIL to CurveNet, **we have now further extended PointMIL to PointNext [1]**, thus adding a second additional backbone during this rebuttal period. PointMIL offered high quality interpretations over CLAIM and PSM on PointNeXt, shown in the table below:
| Method         | AOPCR   | NDCG@n  |
|----------------|---------|---------|
| PSM            | 0.092   | 0.272   |
| CLAIM          | 0.226   | 0.294   |
| **Additive**       | 1.259   | 0.300   |
| **Attention**       | 0.044   | 0.235   |
| **Conjunctive**     | 1.531   | **0.310**   |
| **Instance**       | **2.160**   | 0.285   |

We also evaluated PointMIL's impact on classification performance with PointNeXt. As shown below, **PointMIL improves PointNeXt's classification accuracy on real-world datasets like IntrA and RBC, achieving up to a 2.8% increase in mACC on IntrA.** On ModelNet40, PointMIL maintains competitive performance, with only a 0.3% decrease in mACC and 0.1% increase in oACC compared to the original PointNeXt. While the improvements on ModelNet40 are minor, it is important to note that this is the first inherently interpretable framework for point cloud analysis. There often exists a trade-off between interpretation and classification, however **PointMIL offered interpretability without harming and often improving classification performance.** 

| Method                             | IntrA mACC (↑) | IntrA F1 (↑) | RBC mACC (↑) | RBC F1 (↑) | ModelNet40 mACC (↑) | ModelNet40 oACC (↑) |
|------------------------------------|----------------|--------------|--------------|------------|---------------------|---------------------|
| PointNeXt                          | 91.8           | 94.7         | 86.1         | 87.1       | 90.8                | 93.2                |
| **PointMIL (PointNeXt)**           | **94.6** (+2.8)| **96.2** (+1.5)| **87.6** (+1.5)| **88.2** (+0.4)| **90.5** (-0.3)     | **93.3** (+0.1)     |

---

### **2. Further focus on interpretability:**
We have now further focussed our paper on interpretability. In addition to the first revision, we have increased the number of figures on interpretability to 7, and included lengthy discussions on the different MIL pooling methods and backbones. Therefore, we have restructured the manuscript according to the suggestion by Reviewer 1YBp:
> “Group features through k-nearest neighbours”, “Learned relative positional encoding”, and “Attention on the augmented features” are also widely used in point cloud processing, including PoinTr, Point-BERT, or DGCNN. It is recommended to move these parts into appendix and focus on your interpretation. 

We have moved the feature extractor details to the Appendix (Appendix A).


**We hope that these second revisions address showcase PointMIL's versatility across modern backbones and import the quality of our work. Thank you again for the time and effort in reviewing our work. Please let us know if there are any more concerns.** 


[1]]Guocheng Qian, et al. Pointnext: Revisiting pointnet++ with improved training and scaling strategies. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 23192–23204. Curran Associates, Inc., 2022.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732765673786

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Third revision (Extended to PointMLP (2022) and demonstrated robustness to noise)

comment:
We thank the reviews again for their continued engagement and suggestions. We have uploaded a third revision to address the feedback. Here is a summary of the additional changes made in this revision:

### **PointMLP backbone**
We have included analysis of applying PointMIL to PointMLP [1] on ModelNet40. Specifically, we applied PointMIL to PointMLPElite by adapting the architecture to not downsample the points. We showed the affect of this adaptation on classification results so that any difference in performance can then be attributed to the MIL pooling instead of this adaptation. The results are shown in Table 2 of the updated manuscript and in the table below:
| Method                                  | mACC(↑) | oACC(↑) |
|-----------------------------------------|---------|---------|
| PointMLP [ma2022rethinking]             | **91.3** | **94.1** |
| PointMLPElite                           | 90.9    | 93.6    |
| PointMLPElite$^{\dagger}$               | 90.1    | 92.6    |
| \textsc{PointMIL} (PointMLPElite$^{\dagger}$) | 90.5 (+0.4) | 93.5 (+0.9) |

The results show how removing downsampling originally decays the accuracy. However, applying PointMIL to the adapted architecture improves accuracy back to near the original version. Importantly **this model is now inherently interpretable**. 

We further show how the PointMIL version of PointMLP has similar regions of interpretations to DGCNN, CurveNet, and Transformer on an example Bed and Plant from the ModelNet40 dataset. This is shown in Figure 6 of the updated manuscript.


### **Robustness to noise**

Following the suggestion by Reviewer CZ3U, we have assessed PointMIL's robustness to noise. We initially performed this by comparing PointMIL with the Transformer backbone to other original architectures. However, Reviewer CZ3U pointed out that "previous work [2] has demonstrated that Transformers exhibit superior robustness when facing factors such as noise and rotation". Therefore, as per the suggestion, we performed those additional experiments by applying PointMIL to DGCNN and compared the robustness to noise with the original DGCNN on the IntrA dataset in terms of F1 score and mACC. This is now presented in Figure 11 in Appendix F of the updated manuscript. PointMIL outperformed the original DGCNN by substantial margins, demonstrating the robustness to noise.

To further address this concern, we have provided visualisations of interpretations of PointMIL using Transformer backbone on an Airplane from ModelNet40, shown in Figure 9 in the main of the updated manuscript. Figure 9 shows how, even when noisy
points are added to objects, PointMIL is still able to focus on salient 3D shape motifs, ignoring the noise. 

All changes are shown in blue in the manuscript. **We hope that we have addressed all concerns with this revision. Please let us know about any concerns.**

[1] Xu Ma, et al. Rethinking network design and local geometry in point cloud: A simple residual MLP framework. In International Conference on Learning Representations, 2022

[2]  Sun, Jiachen, et al. "Benchmarking robustness of 3d point cloud recognition against common corruptions." arXiv preprint arXiv:2201.12296 (2022).
</content>
</thread_object>

</thread>
<thread>
creation date:
1733160705294

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Experiments on ScanObjectNN

comment:
Reviewer CZ3U suggested experiments on ScanObjectNN as this dataset will showcase the practical value of PointMIL in real-world scenario's. To address this directly, we have conducted experiments on the hardest version of ScanObjectNN (PB_T50_RS) for DGCNN, PointMLPElite, and Transformer backbones. This concludes our analysis on all three suggested datasets. We have now shown PointMIL on a total of six datasets. Please see the table below:

| Model                         | oACC         | mAcc          |
|-------------------------------|--------------|---------------|
| PointNet (original)              | 68.2         | 63.4          |
| PointNet++ (original)              | 79.1         | 77.6          |
| PointCNN (original)              | 78.5         | 75.1          |
| DGCNN (original)              | 78.1         | 73.6          |
| DGCNN (PointNeXt training)    | 86.0 ± 0.5   |               |
| PointMLPElite (original)      | 83.8 ± 0.6   | 81.8 ± 0.8    |
| **PointMIL (DGCNN)**              | 85.6         | 83.0          |
| **PointMIL (PointMLPElite)**      | 83.0         | 81.4          |
| **PointMIL (Transformer)**        | 83.2         | 80.7          |

We conducted these experiments to directly address the question of PointMIL's feasibility in real-world applications, specifically under the challenging conditions of ScanObjectNN, which includes occlusions, noise, and background clutter. As shown in the table:
- **PointMIL (DGCNN)** (trained with PointNeXt strategies) achieved an **overall accuracy (oACC) of 85.6%** and **mean accuracy (mAcc) of 83.0%**, which falls within the standard deviation range of the DGCNN (PointNeXt training) baseline.  
- **PointMIL (PointMLPElite)** also demonstrated strong performance, with PointMLP results comparable to or within the standard deviation range of their respective original baselines. This highlights PointMIL's adaptability to different backbone architectures.  
-  **PointMIL (Transformer)** (trained with PointNeXt strategies) demonstrated strong performance.
- These results validate that PointMIL can maintain competitive classification accuracy on a real-world dataset with inherent complexities like those in ScanObjectNN further demonstrating practical value.

Finally, the fact that **PointMIL transforms these models to become inherently interpretable is the major strength** for it's practical value in real-world scenarios where model interpretability is fundamental.
</content>
</thread_object>

</thread>
<thread>
creation date:
1733227134640

thread_type:
official_comment

<thread_object>
creator: authors

<content>
comment:
We thank the reviewers for the efforts in reviewing our paper. We believe that we have thoroughly address all concerns raised by every reviewer. We summarise the updates during this rebuttal period below:
1. Experiments on 3 additional modern backbones (CurveNet, PointMLP, and PointNeXt) all showing improved accuracy inherent interpretability with PointMIL. This brings the total number of backbones to 6 and the total number of novel inherently interpretable models to 24.
2. Experiments on ScanObjectNN, demonstrating the practical utility of PointMIL on difficult datasets with noise and occlusion.
3. Validating interpretations with perturbation curves.
4. Substantial expansion on the interpretability sections of the paper.
5. Robustness-to-noise tests, proving that PointMIL increases a backbone's robustness to noise and that interpretations do not change under noisy conditions.
6. Highlighting our adaptation of MIL for point clouds.
7. Clarifications and corrections throughout the manuscript.

While our method is simple, we have rigorously demonstrated its effectiveness. We believe this represents a notable step forward in interpretable point cloud classification research.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper focuses on integrating local interpretability directly into the point cloud classification process using Multiple Instance Learning.  

It has received 4 reviews, with final ratings 6,6,5,5.  The authors provided extensive rebuttals, most notably, included additional experimental results with 3 additional modern backbones (CurveNet, PointMLP, and PointNeXt) and on ScanObjectNN.  They also successful at engaging several rounds of interactions with reviewers.  All reviewers appreciated authors' great efforts at rebuttals, and one reviewer (Reviewer 3) raised the final rating from 5 to 6.

However, despite all the clarification and additional results, Reviewers 1,2,4 still had major concerns.  Reviewer 1 found the technical contribution insignificant, without in-depth analysis or insights into the interpretability, no clear pattern out of different 3D backbones capturing various attentions for point cloud instances.   Reviewer 2 agreed with Reviewer 4 that the model's performance on ModelNet40 and ScanObjectNN was typically weaker than that of the original version, and questioned the solidity of experimental effectiveness.  Reviewer 4 questioned the practicality of this method based on weaker performance on ScanObjectNN, as it avoided downsampling which led to higher computational costs and reduced receptive fields.  Reviewer 4 also thought that this work directly applied multi-instance learning to 3D point clouds without making significant modifications specific to 3D data and considered the contribution insufficient for acceptance.  Based on all these lingering major concerns about the work, the AC recommends rejection.