Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity

Authors: raman  dutt
linus  ericsson
pedro  sanchez
timothy  hospedales
Venue: MIDL_io_2024_Conference
Published: 2024-01-12 19:00:16

Abstract: Foundation models have significantly advanced medical image analysis through the pre-train fine-tune paradigm. Among various fine-tuning algorithms, Parameter-Efficient Fine-Tuning (PEFT) is increasingly utilized for knowledge transfer across diverse tasks, including vision-language and text-to-image generation. However, its application in medical image analysis is relatively unexplored due to the lack of a structured benchmark for evaluating PEFT methods. This study fills this gap by evaluating 17 distinct PEFT algorithms across convolutional and transformer-based networks on image classification and text-to-image generation tasks using six medical datasets of varying size, modality, and complexity. Through a battery of over 700 controlled experiments, our findings demonstrate PEFT's effectiveness, particularly in low data regimes common in medical imaging, with performance gains of up to 22% in discriminative and generative tasks. These recommendations can assist the community in incorporating PEFT into their workflows and facilitate fair comparisons of future PEFT methods, ensuring alignment with advancements in other areas of machine learning and AI.

Main paper: 
 introduction: 
 Medical image analysis has benefited from the deep learning revolution, despite the data-hungry nature of recent foundation models (Dosovitskiy et al., 2021) . The challenge of curating large training datasets in medical image analysis is exacerbated due to privacy restrictions, the long-tailed nature of medical conditions of interest, and high annotation cost (Willemink et al., 2020) . However, the ability to transfer knowledge from one domain into another (transfer learning) has been a key ingredient behind the development of some of the most performant models (Li et al., 2023; Azizi et al., 2021 Azizi et al., , 2023;; Huang et al.; Dutt et al., 2022; Singh and Gorantla, 2020) . Under this paradigm, the pre-training is conducted on either out-of-domain non-medical images or unlabeled medical images followed by fine-tuning on in-domain medical images for the specific task. The emergence of 'foundation models' (Bommasani et al., 2021) has further widened the adoption of this approach.Significant efforts have bolstered the progress in foundation models by scaling them to billions of parameters, hence, the remaining challenge lies in the fine-tuning process that requires striking a delicate balance in adapting the pre-trained model to specialize it for a downstream medical task while avoiding overfitting. This balance has been explored through various fine-tuning algorithms, such as regularized fine-tuning (Xuhong et al., 2018; Gouk et al., 2020) . More recently, Parameter-Efficient Fine-Tuning (PEFT) has gained traction (Xie et al., 2023; Rebuffi et al., 2018; Hu et al., 2022; He et al., 2022a) . The concept involves freezing the original backbone and fine-tuning either a (very small) existing subset or a small new set of parameters. While the NLP and vision communities have greatly benefitted from structured benchmarks for evaluating PEFT algorithms, a similar direction is lacking in medical image analysis. Furthermore, their efficacy in this domain largely remains underexplored.In this work, we present the first structured benchmark for evaluating state-of-the-art PEFT algorithms on diverse medical imaging datasets and tasks. Our evaluation compares 16 different techniques across six medical datasets encompassing both CNN and transformer architectures, discriminative diagnosis tasks, and a novel, first-of-a-kind demonstration of PEFT's effectiveness in a generative medical image synthesis task. We experiment with architectures that match the size of recent foundation models introduced for computer vision and medical image analysis (Kirillov et al., 2023; Chambon et al., 2022a) . Furthermore, we investigate aspects such as the trade-off between PEFT effectiveness and data volume for the task at hand. We establish the first comprehensive comparison benchmark for PEFT in medical vision and offer the community valuable insights into the, currently, best-suited PEFT methods for different types of tasks.Our contributions can be summarised by the following questions and their answers: Q1: How effective is PEFT for low data scenarios? A1: Given a large pre-trained model, benefits from PEFT increase as data volume decreases and model size increases (Sec. 4.1). Q2: Can PEFT improve transfer to discriminative medical tasks? A2: Yes, three methods achieve consistent gains compared to full fine-tuning, two of which also significantly reduce the computational cost of tuning (Sec. 4.2). Q3: Can PEFT improve costly text-to-image generation? A3: Yes, PEFT can provide significant performance gains in image generation quality with much lesser computational cost. (Sec. 4.3). 
related work: 
 Finetuning for Medical Image Analysis. Due to limited availability of data in medical domains, a common paradigm is starting with a deep neural network pre-trained on large natural images, and adapting its weights by fine-tuning (Nima Tajbakhsh, 2016) e.g. via ensembling (Ashnil Kumar, 2017) , active learning (Zongwei Zhou, 2017) or with the aid of expert interactions (Guotai Wang, 2018) . However, tuning recent large foundation models on small datasets -e.g. billions of parameters but only thousands of data points -can cause stability issues and overfitting. Thus, focus has shifted towards what is known as parameter efficient fine-tuning (PEFT), i.e. updating only a small number of parameters while keeping the rest fixed. PEFT for Medical Image Analysis. PEFT techiniques can be categorised into three families, adaptive methods (Hu et al., 2022; Rebuffi et al., 2018; Li et al., 2022; Lian et al., 2022) , selective methods (Ben Zaken et al., 2022; Frankle et al., 2020; Touvron et al., 2022) and prompt tuning (Lester et al., 2021; Jia et al., 2022; Li and Liang, 2021) . A summary of different PEFT methods along with their categorization is given in Table 1 . There has been limited adoption of PEFT tech- niques within medical image analysis. In image segmentation, successes have come from learning prompt tokens in a U-Net Ronneberger et al. (2015) , or adapters designed specifically for dense prediction tasks (Silva-Rodríguez et al., 2023) . On the recently proposed Segment Anything Model (SAM) (Kirillov et al., 2023) -previously unsuccessful in the medical domain -researchers have used PEFT to outperform state-of-the-art methods (Ma et al., 2024; Zhang and Liu, 2023) . Finally, PEFT has also been shown to improve fairness in downstream medical tasks (Dutt et al., 2024) .PEFT for Text-to-Image Generation.Diffusion models (Ho et al., 2020) have led to state-of-the-art results in a variety of tasks such as text-to-image generation (Rombach et al., 2022; Balaji et al., 2022; Saharia et al., 2022) , image synthesis (Dhariwal and Nichol, 2021) , density estimation (Kingma et al., 2021 ) and many others. As in other areas, PEFT methods have been proposed to tune these large models. Key approaches include solely tuning bias terms and learnable scaling factors (Xie et al., 2023) , attention modules (Moon et al.) , adapters (Xiang et al., 2023; Moon et al.) or the singular values of weight matrices (Han et al., 2023) .As of yet, these PEFT methods have not been systematically compared in a medical image analysis setting. We perform the first wide benchmarking study that applies PEFT techniques to diverse tasks in the medical image analysis domain, using state-of-the-art architectures. 
problem definition: 
 Let f be a pre-trained model parameterized by θ, ℓ be a loss function we wish to minimize and D = {(x i ,y i )} N i be the downstream dataset of interest, consisting of inputs x i and their targets y i . Starting from the initialization θ =θ 0 , where θ 0 are the weights from pre-training, our objective is then to optimize by gradient descent the total loss L = 1 N N i=1 ℓ(f(x i ; θ), y i ). Due to resource constraints, such full fine-tuning is not always possible. It can also be suboptimal to tune the entirety of network weights, as many layers may have learned generally applicable features. Parameter-Efficient Fine-Tuning provides options in these cases, which fall into two broad families. Selective methods rely on optimising only a subset of model parameters, ϕ∈θ.This could be a subset of the layers or a specific type of parameter like batch norm. Additive methods instead introduce new parameters such that the full set becomes θ ′ ={θ,ϕ} where ϕ can be as simple as a new classifier layer or carefully designed adapters. For both method families, the update rule is ϕ=ϕ-η∇ ϕ L, where η is the learning rate.We now formally define the different fine-tuning protocols used in the analysis. We begin with a downstream dataset D and a feature extractor f θ (pre-trained CNN (ResNet50) or a ViT (Base/Large/Huge)) expected to produce generalizable representations for diverse tasks. First, we freeze all the weights of this feature extractor and enable either an existing subset or a newly added parameter set according to the fine-tuning protocol.In selective tuning methods, we permit specific parameters to be trainable based on the selected algorithm. For instance, for protocols like BatchNorm and Bias Tuning, the parameters of the 'BatchNorm2d' layers or the 'bias' terms are respectively made trainable. More details including the pseudocode are provided in Appendix (section E).In TSA, our objective is to learn task-specific weights ϕ to obtain the task-adapted classifier f (θ,ϕ) . Next, we minimize the cross-entropy l oss L over the samples in the downstream dataset D w.r.t the task-specific weights ϕ. Li et al. (2022) recommend the parallel adapter configuration.In the SSF method, feature modulation is achieved by introducing scale (γ) and shift (β) parameters following each operation in the model. The previous operation's output is multiplied by the scale parameter through a dot product and combined with the shift factor. Therefore, for a given input x, the output y is calculated using the formula y =γ•x+β.An AdaptFormer module (AdaptMLP) consists of two branches wherein the first branch is identical to the MLP block of a vanilla transformer while the second branch consists of a down-projection (W down ), a ReLU layer, an up-projection (W up ), and a scaling factor (s). The adapted features are combined with the original features entering the AdaptMLP block through a residual connection.LoRA is based on the concept that, during adaptation, weight updates exhibit low intrinsic rank. Consequently, when a pre-trained weight matrix W 0 is updated, the change (∆W ) is characterized by a low-rank decomposition operation with rank r, as shown in Eq. 1 where B ∈R d×r and A∈R r×k , W 0 +∆W =W 0 +BA.(1)SV-Diff performs Singular Value Decomposition (SVD) of the weight matrices of a pretrained diffusion model and optimizes the spectral shift (δ), defined as the difference between singular values and of the updated and original weight matrix. 
how effective is peft for low data scenarios?: 
 Setup. We utilized the HAM10000 dataset (Tschandl et al., 2018) and employed three distinct fine-tuning methods, namely Full Fine-tuning, BitFit, and LoRA, in combination with two different encoders, ViT Base and ViT Large. F1-Score was measured at various dataset sizes, commencing with the entire sample size of 7,511 images (100%) and progressively reducing it to a minimum of 75 images (1%). To account for potential variability in the results, we report the average performance across three random seeds.Results. The results are shown in Figure 1 . For ViT Base (left), we find that when using 100% of available downstream data, full fine-tuning is optimal, closely followed by LoRA. As the availability decreases, however, the benefits from PEFT approaches increase. The crossover is at 50%, when all approaches are approximately equal. For smaller data sizes, both PEFT approaches consistently outperform full FT, with LoRA providing gains of up to 6% over the baseline. For ViT Large, the trend is similar, but the crossover now differs between the PEFT approaches. LoRA overtakes the baseline as early as 80% while BitFit is only better at data volumes below 20%. The take-home message here is that when data are scarce and the upstream model is large, it becomes especially important to consider parameter-efficient tuning.Setup. In our discriminative experiments, we use five diverse datasets widely recognized in the medical image analysis community for image classification tasks, BreastUS (Al-Dhabyani et al., 2020), HAM10000 (Tschandl et al., 2018) , Fitzpatrick17K (Groh et al., 2021 (Groh et al., , 2022)) Results. We present the results for ResNet-50 in Table 2 . Given its convolutional architecture, ResNet-50 is compatible with certain PEFT methods but not others. Overall, full fine-tuning tends to outperform basic linear probing. Observations from the BreastUS and SMDG datasets indicate that most PEFT methods enhance performance beyond the full FT baseline. The SSF method, despite only tuning 60K parameters (0.25%), improves performance by up to 22%. While gains on HAM10000, FitzPatrick and Pneumonia are more modest, the previous section has discussed how these results could potentially vary with changes in data volume and model size. Overall, SSF emerges as the top-performing method based on average F1 score and ranking. Full fine-tuning and TSA present a close tie with the latter emerging on top. BatchNorm and bias tuning perform better than linear probing which turns out to be the worst strategy. Overall, the greatest gains are observed in the smallest dataset (BreastUS), however, the performance gap between full fine-tuning and PEFT methods minimizes with an increase in dataset size. For Transformer models in Tab. 3, the situation is similar. The biggest gains over full FT are on BreastUS and SMDG, while linear probing underperforms here as well. The best PEFT method is LoRA, for both average F1 score and rank, across all five datasets. AdaptFormer does not perform well and even falls behind linear probing for ViT Large. This can be attributed to the fact that this method was mainly designed for video recognition tasks. We also see that the benefits of PEFT increase slightly as the model size increases, with a 4% improvement for ViT Base going to 6% for ViT Huge. This is an interesting finding, and agrees with Sec. 4.1, as the proportion of parameters tuned actually decreases for the larger models.Figure 3 illustrates the trade-off between each method's performance and parameter count. This comparison is crucial as different applications may prioritize either superior performance or computational efficiency. For the results produced by the ResNet50 (shown on the left), each PEFT method lies on the Pareto frontier, indicating that a specific method could be selected based on the prioritization of either performance or cost. Remarkably, the SSF method stands out by delivering high performance at a significantly reduced cost. In the case of the ViT-B model, LoRA emerges as the prominent choice, outpacing SSF while maintaining a similar computational expense.To answer our question can PEFT Improve Transfer to Discriminative Medical Tasks? Yes, TSA, SSF and LoRA provide consistent improvements over full fine-tuning while requiring as little as 0.25% of parameters.Setup.We use the MIMIC-CXR dataset (v. 2.0.0) (Johnson et al., 2019) . Following the recommendations of Chambon et al. (2022b) , we fine-tune only the U-Net component (keeping text-encoder and VAE frozen) of the stable diffusion pipeline for different sizes of the downstream dataset (110K, 55K, and 11K, representing 100%, 50% and 10% of the entire dataset). For analysis, we compare the full-finetuning of U-Net with 7 different PEFT methods and report the For all data volumes, several PEFT methods outperformed full fine-tuning with significant differences in FID scores. A particularly interesting observation is that simple strategies such as fine-tuning just the bias or normalization layers are amongst the best performers, assuming first and third ranks respectively. Other PEFT methods designed exclusively for text-to-image generation tasks (SV-Diff and DiffFit) follow closely and also outperform full fine-tuning. Interestingly, LoRA, the best-performing method for classification tasks fails to provide any benefits in image generation. Overall, PEFT shows strong promise in improving the medical image generation quality across different data volumes. 
conclusion: 
 We performed the first, thorough evaluation of parameter-efficient fine-tuning for the medical image analysis domain covering a wide range of algorithms, architectures, datasets, and tasks. For discriminative tasks, the benefits of PEFT increase with decreasing data volume and increasing model size. Furthermore, The benefits of PEFT are especially prominent for low to medium-scale datasets, which are particularly common in the medical domain. SSF and LoRA emerged as the best-performing methods for CNNs and ViTs respectively in our analysis. For generative tasks, simple strategies such as Bias Tuning and tailored methods such as SV-Diff provide significant performance gains over conventional strategies. With rapid progress in studying efficient fine-tuning algorithms, this benchmark would allow easy integration and evaluation of new PEFT methods on diverse medical tasks in future.  
 
 Appendix:  


Decision: All reviewers unanimously agreed to accept the work for presentation at the 2024 MIDL meeting. The authors are requested to revise the camera-ready version in accordance with the feedback provided in the rebuttal.

Reviews: review: paper_summary: paper_summary: This paper aims to build a benchmark comparison of different fine-tuning (FT) strategies for medical image classification and synthesis, using both convolutional and transformer backbones. Comprehensive fine-tuning experiments were conducted to identify the best-performing Parameter-Efficient Fine-Tuning (PEFT) strategies for different tasks.
main_review: main_review: The authors will add more details regarding the experimental hyper parameters. The authors also emphasize the focus of this work as evaluating various FT methods rather than outperforming SOTA methods. I decide to increase my rating to '5: strong accept'.
strength_weakness: strength_weakness: - Some training details were missing, such as the batch size and optimizer used. Were the same batch size and optimizer used for different PEFTs?
- In each dataset, only FT-based methods were trained and compared without considering SOTA methods for each dataset, such as those without pre-training but with an appropriate model and parameter size.
questions: questions: Please add training details for the different methods used in the study. Also, discuss SOTA methods in each dataset and how the best performing PEFTs compare to these methods.
paper_summary: paper_summary: This paper explores the use of Parameter-Efficient Fine-Tuning (PEFT) methods for medical image analysis, and in particular, for image classification and text-to-image generation tasks. This work evaluates several popular PEFT methods for different tasks, under several data regimes, adapting models pre-trained on large natural image datasets.
main_review: main_review: I understand that the length limitations of the article do not allow providing a more detailed analysis of PEFT methods in other medical imaging applications, such as image segmentation, or the use of domain-specific pre-trained networks. I think the paper does provide enough experimental evidence to show the potential of PEFT on medical image analysis and constitutes a considerable contribution to the current literature. Therefore, I will keep the preliminary rating.
strength_weakness: strength_weakness: - Although the authors talk about SAM, and some works on segmentation, only image classification and image generation tasks are addressed. 
- The core idea of PEFT implies re-using rich pre-trained features and adapting them to downstream tasks in an efficient fashion. Under this scope, using features pre-trained on natural images supposes a large domain shift to the target domain. For some of the target tasks, there already exist domain-specific pre-trained models (e.g. MedCLIP [a] for Chest X-Ray), which may provide a more data- and parameter-efficient adaptation.

[a] Wang, Zifeng and Wu, Zhenbang and Agarwal, Dinesh and Sun, Jimeng. (2022). MedCLIP: Contrastive Learning from Unpaired Medical Images and Texts. EMNLP'22.
questions: questions: Please, see Weaknesses.
paper_summary: paper_summary: This paper proposes the first extensive benchmark of Parameter-Efficient Fine-Tuning (PEFT) methods on diverse medical imaging datasets and tasks. Through these experiments, the paper attempts to answer several keys practical keys questions such as:
- How well PEFT methods are effective when considering low data regimes and large models?
- Are PEFT methods able to improve transfer to medical discriminative tasks?
- Can PEFT Improve Costly Text-to-Image Generation?
main_review: main_review: Given the limited rebuttal Time, I understand that authors could not run the experiment even. I will keep my grade as it is (weak accept) but I think this work is worth being published and would be useful for the community.
strength_weakness: strength_weakness: I don't have major concerns. Even though I know that experiments cannot be carried out for all datasets and tasks, dense tasks such as object detection, tracking, semantic segmentation, and instance segmentation are extremely popular in the medical imaging community. Therefore it would have been very interesting to see how well large pretrained models can be adapted with PEFT methods for some of these dense tasks. I think it would make the paper even more helpful for the community.
questions: questions: Please address the concerns from the weaknesses section.

Comments: title: Regarding Oral Presentation Decision for Accepted Paper at MIDL 2024
paper is open for discussions
Addressing only Classification and Generative Tasks
Evaluating Domain-specific Pre-trained Models and Benchmarking only Classification and Generative Tasks
Training Details and Comparison with SoTA
Thank you for the review
comment: Dear Area Chair mk4u

Thank you very much for recommending an acceptance of our paper. 

Given the favourable evaluations, with scores of 4, 5, and 5, and particularly noting one reviewer's (reviewer 7zWN) recommendation for oral presentation, I am eager to know if the decision for oral presentations has been made.

Looking forward to your reply.

Thanks again and Warm Regards
Thanks for the detailed response!

I do not fully agree with the claim that pre-training schemes such as MAE and CLIP are necessarily better than supervised pre-training. Supervised pre-training is yet an ongoing evolving field, and if properly done, it is largely competitive, according to recent literature [1,2]. Nevertheless, although being an interesting discussion theme, this topic does not directly affect the paper under review.

[1] Bulent et al. "No Reason for No Supervision: Improved Generalization in Supervised Models". ICLR 2023.

[2] Wang et al. "Revisiting the Transferability of Supervised Pretraining: an MLP Perspective". CVPR 2022.

I understand that the length limitations of the article do not allow providing a more detailed analysis of PEFT methods in other medical imaging applications, such as image segmentation, or the use of domain-specific pre-trained networks. I think the paper does provide enough experimental evidence to show the potential of PEFT on medical image analysis and constitutes a considerable contribution to the current literature. Therefore, I will keep the preliminary rating.
Given the limited rebuttal Time, I understand that authors could not run the experiment I wanted. I will keep my grade as it is (weak accept)
Dear Reviewers
The authors have submitted their rebuttal addressing the raised questions. The paper remains open for further discussion and engagement.
Thank you for your time and effort in providing an insightful review and recognizing our work as _well-written_ and _exhaustive_. We have addressed your comments below.

**Addressing only classification and generative tasks:** We agree it would be interesting to include detection and segmentation in our analysis and expect that PEFT methods will also benefit dense medical imaging tasks as they have benefitted dense tasks outside medical imaging [1]. However, our broader goal was to evaluate the two main categories of tasks: _discriminative_, through image classification and _generative_, through text-to-image generation. Nevertheless, given the constraints on space and author feedback time, we must leave this for a future extension of this work.

**References**
1. Chen, Hao, et al. "Conv-adapter: Exploring parameter efficient transfer learning for convnets." arXiv preprint arXiv:2208.07463 (2022).
Thank you for your time and feedback for providing this review. We have addressed the comments below.

**Evaluating Domain-specific Pre-trained Models:** We agree that exploring domain-specific pre-trained models would be interesting, and could potentially improve the results further. However, the majority of studies today have used ImageNet1K or ImageNet21K as pre-training data, so we used these pre-trained models to be in line with the most common practice of prior work. We also already presented additional results on using different (and stronger than supervised learning) pre-training schemes including MAE [1] and CLIP [2] in the Appendix. Our combined results (Tables 2,3 and 5) show the dominance of PEFT strategies across different pre-training schemes. Hence, we believe that we would see a similar pattern for domain-specific pre-training objectives as well. However, given the limited author feedback window and paper space, we must leave this to future work.

**Addressing only classification and generative tasks:** We agree this would be interesting to know and expect that PEFT methods will also benefit dense medical imaging tasks as they have benefitted dense tasks outside of medical imaging [3]. Our broader goal was to evaluate the two main categories of tasks: _discriminative_, through image classification and _generative_, through text-to-image generation. Nevertheless, given the constraints on space and author feedback time, we must leave this for a future extension of this work.

**References**
1. He, Kaiming, et al. "Masked autoencoders are scalable vision learners." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

2. Radford, Alec, et al. "Learning transferable visual models from natural language supervision." International conference on machine learning. PMLR, 2021.

3. Chen, Hao, et al. "Conv-adapter: Exploring parameter efficient transfer learning for convnets." arXiv preprint arXiv:2208.07463 (2022).
Thank you for the insightful review and for identifying our paper as a _comprehensive analysis_ supported with _good visualizations_ and _clear conclusions_. We have addressed your comments below.

**Training Details:** For each experiment, we used the same batch size of **512** and optimizer **AdamW** [1]. From our initial experiments, we observed that the choice of optimizer does not affect the fine-tuning performance and hence, we selected one of the most widely adopted optimizers for both classification and generative tasks. 

**Additional Details:** We observed that fine-tuning of PEFT methods shows a preference for larger learning rates (about a magnitude higher than the full fine-tuning). However, since each fine-tuning strategy, model architecture, and dataset might benefit from a different learning rate, we relied on a common HPO procedure to obtain the optimal learning rate for each competitor, in order to perform a fair comparison. We will add further details about the HPO procedure in the Appendix of the final version of the paper. 

**Comparison with SoTA methods:** The primary objective of this study was to evaluate and compare various fine-tuning strategies, revealing the effectiveness of PEFT methods specifically tailored for medical tasks. While prior studies may have employed diverse techniques such as advanced architectures, regularizers or augmentations to achieve state-of-the-art (SoTA) outcomes on specific datasets, it is essential to note that such endeavours diverge from the focus of our investigation. Our goal is not to claim/create a SoTA result on a specific benchmark but to understand the potential for PEFT with carefully controlled experiments across a range of benchmarks. Our findings could of course be applied in conjunction with the tools employed by existing SoTA solutions, and would likely help to improve their performance going forward.

**References:**
1. Loshchilov, Ilya and Frank Hutter. “Decoupled Weight Decay Regularization.” International Conference on Learning Representations (2017).
We would like to sincerely thank all the reviewers for their time and effort in providing constructive criticism of our work. We appreciate that reviewers recognize several merits of our paper such as _comprehensive experiments_, _well-structured and easy-to follow_, _good starting point for future benchmarks_ ,_sound motivation_, and _results serving as strong guidelines for the community_. 

We have addressed the following main comments of the reviewers.

1. Including training details and comparison with existing SoTA (Reviewer mQqj)
2. Evaluating Domain-specific Pre-trained Models (Reviewer 7zWN)
3. Benchmarking only Classification and Generative Tasks (Reviewers 7zWN and ZQbg)