Publications | Rishi Veerapaneni

I broadly work on search-based motion planning for single and multi-agent systems. My specific research interest is in (1) designing better heuristic search algorithms, (2) multi-agent motion planning and coordination (e.g. MAPF), and (3) combining search with machine learning.

2024

Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality

Yorai Shaoul* , Rishi Veerapaneni*, Maxim Likhachev , and Jiaoyang Li

In Proceedings of the International Symposium on Combinatorial Search (SoCS) , 2024

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Multi-Robot-Arm Motion Planning (M-RAMP) is a challenging problem featuring complex single-agent planning and multi-agent coordination. Recent advancements in extending the popular Conflict-Based Search (CBS) algorithm have made large strides in solving Multi-Agent Path Finding (MAPF) problems. However, fundamental challenges remain in applying CBS to M-RAMP. A core challenge is the existing reliance of the CBS framework on conservative "complete" constraints. These constraints ensure solution guarantees but often result in slow pruning of the search space – causing repeated expensive single-agent planning calls. Therefore, even though it is possible to leverage domain knowledge and design incomplete M-RAMP-specific CBS constraints to more efficiently prune the search, using these constraints would render the algorithm itself incomplete. This forces practitioners to choose between efficiency and completeness. In light of these challenges, we propose a novel algorithm, Generalized ECBS, aimed at removing the burden of choice between completeness and efficiency in MAPF algorithms. Our approach enables the use of arbitrary constraints in conflict-based algorithms while preserving completeness and bounding sub-optimality. This enables practitioners to capitalize on the benefits of arbitrary constraints and opens a new space for constraint design in MAPF that has not been explored. We provide a theoretical analysis of our algorithms, propose new "incomplete" constraints, and demonstrate their effectiveness through experiments in M-RAMP.
@inproceedings{shaoul2024generalized_ecbs, title = {Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality}, author = {Shaoul*, Yorai and Veerapaneni*, Rishi and Likhachev, Maxim and Li, Jiaoyang}, booktitle = {Proceedings of the International Symposium on Combinatorial Search (SoCS)}, volume = {17}, pages = {109-117}, year = {2024}, doi = {10.1609/socs.v17i1.31548}, url = {https://ojs.aaai.org/index.php/SOCS/article/view/31548}, }
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities

He Jiang , Yulun Zhang , Rishi Veerapaneni, and Jiaoyang Li

In Proceedings of the International Symposium on Combinatorial Search (SoCS) , 2024

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Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new goals to agents. We present our winning approach to the 2023 League of Robot Runners LMAPF competition, which leads us to several interesting research challenges and future directions. In this paper, we outline three main research challenges. The first challenge is to search for high-quality LMAPF solutions within a limited planning time (e.g., 1s per step) for a large number of agents (e.g., 10,000) or extremely high agent density (e.g., 97.7%). We present future directions such as developing more competitive rule-based and anytime MAPF algorithms and parallelizing state-of-the-art MAPF algorithms. The second challenge is to alleviate congestion and the effect of myopic behaviors in LMAPF algorithms. We present future directions, such as developing moving guidance and traffic rules to reduce congestion, incorporating future prediction and real-time search, and determining the optimal agent number. The third challenge is to bridge the gaps between the LMAPF models used in the literature and real-world applications. We present future directions, such as dealing with more realistic kinodynamic models, execution uncertainty, and evolving systems.
@inproceedings{jiang2024scaling_lifelong_mapf, title = {Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities}, author = {Jiang, He and Zhang, Yulun and Veerapaneni, Rishi and Li, Jiaoyang}, booktitle = {Proceedings of the International Symposium on Combinatorial Search (SoCS)}, volume = {17}, pages = {234-242}, year = {2024}, doi = {10.1609/socs.v17i1.31565}, url = {https://ojs.aaai.org/index.php/SOCS/article/view/31565}, }
From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS

Yu Wu , Rishi Veerapaneni, Jiaoyang Li , and Maxim Likhachev

arXiv preprint arxiv:2404.15137, 2024

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The majority of multi-agent path finding (MAPF) methods compute collision-free space-time paths which require agents to be at a specific location at a specific discretized timestep. However, executing these space-time paths directly on robotic systems is infeasible due to real-time execution differences (e.g. delays) which can lead to collisions. To combat this, current methods translate the space-time paths into a temporal plan graph (TPG) that only requires that agents observe the order in which they navigate through locations where their paths cross. However, planning space-time paths and then post-processing them into a TPG does not reduce the required agent-to-agent coordination, which is fixed once the space-time paths are computed. To that end, we propose a novel algorithm Space-Order CBS that can directly plan a TPG and explicitly minimize coordination. Our main theoretical insight is our novel perspective on viewing a TPG as a set of space-visitation order paths where agents visit locations in relative orders (e.g. 1st vs 2nd) as opposed to specific timesteps. We redefine unique conflicts and constraints for adapting CBS for space-order planning. We experimentally validate how Space-Order CBS can return TPGs which significantly reduce coordination, thus subsequently reducing the amount of agent-agent communication and leading to more robustness to delays during execution.
@article{wu2024space_order_cbs, title = {From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS}, author = {Wu, Yu and Veerapaneni, Rishi and Li, Jiaoyang and Likhachev, Maxim}, year = {2024}, journal = {arXiv preprint arxiv:2404.15137}, eprint = {2404.15137}, archiveprefix = {arXiv}, primaryclass = {cs.MA}, }
A Data Efficient Framework for Learning Local Heuristics

Rishi Veerapaneni*, Jonathan Park* , Muhammad Suhail Saleem , and Maxim Likhachev

In Proceedings of the International Symposium on Combinatorial Search (SoCS) , 2024

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With the advent of machine learning, there have been several recent attempts to learn effective and generalizable heuristics. Local Heuristic A* (LoHA*) is one recent method that instead of learning the entire heuristic estimate, learns a "local" residual heuristic that estimates the cost to escape a region (Veerapaneni et al 2023). LoHA*, like other supervised learning methods, collects a dataset of target values by querying an oracle on many planning problems (in this case, local planning problems). This data collection process can become slow as the size of the local region increases or if the domain requires expensive collision checks. Our main insight is that when an A* search solves a start-goal planning problem it inherently ends up solving multiple local planning problems. We exploit this observation to propose an efficient data collection framework that does <1/10th the amount of work (measured by expansions) to collect the same amount of data in comparison to baselines. This idea also enables us to run LoHA* in an online manner where we can iteratively collect data and improve our model while solving relevant start-goal tasks. We demonstrate the performance of our data collection and online framework on a 4D (x,y,θ,v) navigation domain.
@inproceedings{veerapaneni2024data_efficient_local_heuristics, title = {A Data Efficient Framework for Learning Local Heuristics}, author = {Veerapaneni*, Rishi and Park*, Jonathan and Saleem, Muhammad Suhail and Likhachev, Maxim}, doi = {10.1609/socs.v17i1.31563}, booktitle = {Proceedings of the International Symposium on Combinatorial Search (SoCS)}, volume = {17}, pages = {223-227}, year = {2024}, url = {https://ojs.aaai.org/index.php/SOCS/article/view/31563}, }
MAPF in 3D Warehouses: Dataset and Analysis

Qian Wang* , Rishi Veerapaneni*, Yu Wu , Jiaoyang Li , and Maxim Likhachev

International Conference on Automated Planning and Scheduling (ICAPS), 2024

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Recent works have made significant progress in multi-agent path finding (MAPF), with modern methods being able to scale to hundreds of agents, handle unexpected delays, work in groups, etc. The vast majority of these methods have focused on 2D "grid world" domains. However, modern warehouses often utilize multi-agent robotic systems that can move in 3D, enabling dense storage but resulting in a more complex multi-agent planning problem. Motivated by this, we introduce and experimentally analyze the application of MAPF to 3D warehouse management, and release the first (see http://mapf.info/index.php/Main/Benchmarks) open-source 3D MAPF dataset. We benchmark two state-of-the-art MAPF methods, EECBS and MAPF-LNS2, and show how different hyper-parameters affect these methods across various 3D MAPF problems. We also investigate how the warehouse structure itself affects MAPF performance. Based on our experimental analysis, we find that a fast low-level search is critical for 3D MAPF, EECBS’s suboptimality significantly changes the effect of certain CBS techniques, and certain warehouse designs can noticeably influence MAPF scalability and speed. An additional important observation is that, overall, the tested 2D MAPF techniques scaled well to 3D warehouses and demonstrate how the MAPF community’s progress in 2D can generalize to 3D warehouses.
@article{wang2024mapf_3d_warehouse, title = {MAPF in 3D Warehouses: Dataset and Analysis}, volume = {34}, url = {https://ojs.aaai.org/index.php/ICAPS/article/view/31525}, doi = {10.1609/icaps.v34i1.31525}, number = {1}, journal = {International Conference on Automated Planning and Scheduling (ICAPS)}, author = {Wang*, Qian and Veerapaneni*, Rishi and Wu, Yu and Li, Jiaoyang and Likhachev, Maxim}, year = {2024}, pages = {623-632}, }
Improving Learnt Local MAPF Policies with Heuristic Search

Rishi Veerapaneni*, Qian Wang* , Kevin Ren* , Arthur Jakobsson* , Jiaoyang Li , and Maxim Likhachev

International Conference on Automated Planning and Scheduling (ICAPS), 2024

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Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically employ heuristic search to find solutions for hundreds of agents but are typically centralized and can struggle to scale when run with short timeouts. Machine learning (ML) approaches that learn policies for each agent are appealing as these could enable decentralized systems and scale well while maintaining good solution quality. Current ML approaches to MAPF have proposed methods that have started to scratch the surface of this potential. However, state-of-the-art ML approaches produce “local" policies that only plan for a single timestep and have poor success rates and scalability. Our main idea is that we can improve a ML local policy by using heuristic search methods on the output probability distribution to resolve deadlocks and enable full horizon planning. We show several model-agnostic ways to use heuristic search with learnt policies that significantly improve the policies’ success rates and scalability. To our best knowledge, we demonstrate the first time ML-based MAPF approaches have scaled to high congestion scenarios (e.g. 20% agent density).
@article{veerapaneni2024improving_mapf_policies_with_search, title = {Improving Learnt Local MAPF Policies with Heuristic Search}, volume = {34}, url = {https://ojs.aaai.org/index.php/ICAPS/article/view/31522}, doi = {10.1609/icaps.v34i1.31522}, number = {1}, journal = {International Conference on Automated Planning and Scheduling (ICAPS)}, author = {Veerapaneni*, Rishi and Wang*, Qian and Ren*, Kevin and Jakobsson*, Arthur and Li, Jiaoyang and Likhachev, Maxim}, year = {2024}, pages = {597-606}, }
Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution

Yifan Su , Rishi Veerapaneni, and Jiaoyang Li

AAAI Conference on Artificial Intelligence (AAAI), 2024

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The Multi-Agent Path Finding (MAPF) problem involves planning collision-free paths for multiple agents in a shared environment. The majority of MAPF solvers rely on the assumption that an agent can arrive at a specific location at a specific timestep. However, real-world execution uncertainties can cause agents to deviate from this assumption, leading to collisions and deadlocks. Prior research solves this problem by having agents follow a Temporal Plan Graph (TPG), enforcing a consistent passing order at every location as defined in the MAPF plan. However, we show that TPGs are overly strict because, in some circumstances, satisfying the passing order requires agents to wait unnecessarily, leading to longer execution time. To overcome this issue, we introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG), which allows switching passing orders during execution to avoid unnecessary waiting time. We design two anytime algorithms for constructing a BTPG: BTPG-naïve and BTPG-optimized. Experimental results show that following BTPGs consistently outperforms following TPGs, reducing unnecessary waits by 8-20%.
@article{su2024bidirectional_tpg, title = {Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution}, volume = {38}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/29706}, doi = {10.1609/aaai.v38i16.29706}, number = {16}, journal = {AAAI Conference on Artificial Intelligence (AAAI)}, author = {Su, Yifan and Veerapaneni, Rishi and Li, Jiaoyang}, year = {2024}, pages = {17559-17566}, }

2023

Preprocessing-Based Planning for Utilizing Contacts in Semi-Structured High-Precision Insertion Tasks

Muhammad Suhail Saleem , Rishi Veerapaneni, and Maxim Likhachev

IEEE Robotics and Automation Letters (RAL), 2023

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In manipulation tasks like plug insertion or assembly that have low tolerance to errors in pose estimation (errors of the order of 2 mm can cause task failure), the utilization of touch/contact modality can aid in accurately localizing the object of interest. Motivated by this, in this work we model high-precision insertion tasks as planning problems under pose uncertainty, where we effectively utilize the occurrence of contacts (or the lack thereof) as observations to reduce uncertainty and reliably complete the task. We present a preprocessing-based planning framework for high-precision insertion in repetitive and time-critical settings, where the set of initial pose distributions (identified by a perception system) is finite. The finite set allows us to enumerate the possible planning problems that can be encountered online and preprocess a database of policies. Due to the computational complexity of constructing this database, we propose a general experience-based POMDP solver, E-RTDP-Bel, that uses the solutions of similar planning problems as experience to speed up planning queries and use it to efficiently construct the database. We show that the developed algorithm speeds up database creation by over a factor of 100, making the process computationally tractable. We demonstrate the effectiveness of the proposed framework in a real-world plug insertion task in the presence of port position uncertainty and a pipe assembly task in simulation in the presence of pipe pose uncertainty.
@article{saleem2023preprocessing_contacts_planning, author = {Saleem, Muhammad Suhail and Veerapaneni, Rishi and Likhachev, Maxim}, journal = {IEEE Robotics and Automation Letters (RAL)}, title = {Preprocessing-Based Planning for Utilizing Contacts in Semi-Structured High-Precision Insertion Tasks}, year = {2023}, volume = {8}, number = {11}, pages = {6947-6954}, keywords = {Planning;Task analysis;Databases;Robot sensing systems;Uncertainty;Plugs;Location awareness;Manipulation planning;planning under uncertainty;reactive and sensor -based planning}, doi = {10.1109/LRA.2023.3309592}, }
Learning Local Heuristics for Search-Based Navigation Planning

Rishi Veerapaneni, Muhammad Suhail Saleem , and Maxim Likhachev

International Conference on Automated Planning and Scheduling (ICAPS), 2023

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Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can directly plan long horizon paths, they often require careful hand designing of informative heuristic functions. Recent works have started bypassing hand designed heuristics by using machine learning to learn heuristic functions that guide the search algorithm. While these methods can learn complex heuristic functions from raw input, they i) require significant training and ii) do not generalize well to new maps and longer horizon paths. Our contribution is showing that instead of learning a global heuristic estimate, we can define and learn local heuristics which results in a significantly smaller learning problem and improves generalization. We show that using such local heuristics can reduce node expansions by 2-20x while maintaining bounded suboptimality, are easy to train, and generalize to new maps & long horizon plans.
@article{veerapaneni2023local_heuristic_navigation, title = {Learning Local Heuristics for Search-Based Navigation Planning}, volume = {33}, url = {https://ojs.aaai.org/index.php/ICAPS/article/view/27245}, doi = {10.1609/icaps.v33i1.27245}, number = {1}, journal = {International Conference on Automated Planning and Scheduling (ICAPS)}, author = {Veerapaneni, Rishi and Saleem, Muhammad Suhail and Likhachev, Maxim}, year = {2023}, pages = {634-638}, }
Effective Integration of Weighted Cost-to-Go and Conflict Heuristic within Suboptimal CBS

Rishi Veerapaneni, Tushar Kusnur , and Maxim Likhachev

AAAI Conference on Artificial Intelligence (AAAI), 2023

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Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few methods modifying the low level planner. Typically low level planners in existing CBS methods use an unweighted cost-to-go heuristic, with suboptimal CBS methods also using a conflict heuristic to help the high level search. In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants. In particular, one of these variants can obtain large speedups, 2-100x, across several scenarios and suboptimal CBS methods. Importantly, we discover that performance is related not to the weighted cost-to-go heuristic but rather to the relative conflict heuristic weight’s ability to effectively balance low-level and high-level work. Additionally, to the best of our knowledge, we show the first theoretical relation of prioritized planning and bounded suboptimal CBS and demonstrate that our methods are their natural generalization. Update March 2024: We found that the relative speedup decreases to around 1.2-10x depending on how the conflict heuristic is computed (see appendix for more details).
@article{veerapaneni2023effective_integration_weecbs, title = {Effective Integration of Weighted Cost-to-Go and Conflict Heuristic within Suboptimal CBS}, volume = {37}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/26381}, doi = {10.1609/aaai.v37i10.26381}, number = {10}, journal = {AAAI Conference on Artificial Intelligence (AAAI)}, author = {Veerapaneni, Rishi and Kusnur, Tushar and Likhachev, Maxim}, year = {2023}, pages = {11691-11698}, }

2022

Minimizing Coordination in Multi-Agent Path Finding with Dynamic Execution

Aidan Wagner* , Rishi Veerapaneni*, and Maxim Likhachev

AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2022

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Multi-agent Path Finding (MAPF) is an important problem in large games with many dynamic agents that need to follow space-time trajectories without inter-agent collisions. Modern MAPF solvers plan assuming that agents directly follow the space-time trajectories at known constant speeds without delays or speedups, resulting in rigid plans which need to be replanned if there are changes during execution. Instead we would like agents to be able to follow their computed paths with dynamic velocities while requiring minimal coordination with others to prevent collisions and deadlocks. One way to address this problem is to first produce collision free space-time paths and then compute a coordination controller that prevents collisions and deadlock during dynamic execution. This two step process prevents fully minimizing coordination as the initially planned space-time paths do not reason about coordination and can be arbitrarily bad. We introduce a novel paradigm and show how planning in space-coordination level, rather than space-time, allows us to simultaneously plan paths and a coordination controller. Our method, Space-Level Conflict-Based Search (SL-CBS), builds on the Conflict-Based Search framework and allows us to reason explicitly about coordination, producing paths as well as a coordination controller with bounded suboptimal minimal coordination. We show experimentally that this results in a 20-50% reduction in coordination compared to the closest state of the art solver.
@article{wagner2022space_level_csb, title = {Minimizing Coordination in Multi-Agent Path Finding with Dynamic Execution}, volume = {18}, url = {https://ojs.aaai.org/index.php/AIIDE/article/view/21948}, doi = {10.1609/aiide.v18i1.21948}, number = {1}, journal = {AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE)}, author = {Wagner*, Aidan and Veerapaneni*, Rishi and Likhachev, Maxim}, year = {2022}, pages = {61-69}, }
Non-Blocking Batch A* (Technical Report)

Rishi Veerapaneni, and Maxim Likhachev

arXiv preprint arxiv:2208.07031, 2022

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Heuristic search has traditionally relied on hand-crafted or programmatically derived heuristics. Neural networks (NNs) are newer powerful tools which can be used to learn complex mappings from states to cost-to-go heuristics. However, their slow single inference time is a large overhead that can substantially slow down planning time in optimized heuristic search implementations. Several recent works have described ways to take advantage of NN’s batch computations to decrease overhead in planning, while retaining bounds on (sub)optimality. However, all these methods have used the NN heuristic in a "blocking" manner while building up their batches, and have ignored possible fast-to-compute admissible heuristics (e.g. existing classically derived heuristics) that are usually available to use. We introduce Non-Blocking Batch A* (NBBA*), a bounded suboptimal method which lazily computes the NN heuristic in batches while allowing expansions informed by a non-NN heuristic. We show how this subtle but important change can lead to substantial reductions in expansions compared to the current blocking alternative, and see that the performance is related to the information difference between the batch computed NN and fast non-NN heuristic.
@article{veerapaneni2022nonblocking, title = {Non-Blocking Batch A* (Technical Report)}, author = {Veerapaneni, Rishi and Likhachev, Maxim}, journal = {arXiv preprint arxiv:2208.07031}, year = {2022}, }

2020

Entity Abstraction in Visual Model-Based Reinforcement Learning

Rishi Veerapaneni*, John D. Co-Reyes* , Michael Chang* , Michael Janner , Chelsea Finn , Jiajun Wu , Joshua Tenenbaum , and Sergey Levine

In Conference on Robot Learning (CoRL) , 2020

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We present OP3, a framework for model-based reinforcement learning that acquires object representations from raw visual observations without supervision and uses them to predict and plan. To ground these abstract representations of entities to actual objects in the world, we formulate an interactive inference algorithm which incorporates dynamic information in the scene. Our model can handle a variable number of entities by symmetrically processing each object representation with the same locally-scoped function. On block-stacking tasks, OP3 can generalize to novel block configurations and more objects than seen during training, outperforming both a model that assumes access to object supervision and a state-of-the-art video prediction model.
@inproceedings{veerapaneni20entity_abstraction, title = {Entity Abstraction in Visual Model-Based Reinforcement Learning}, author = {Veerapaneni*, Rishi and Co-Reyes*, John D. and Chang*, Michael and Janner, Michael and Finn, Chelsea and Wu, Jiajun and Tenenbaum, Joshua and Levine, Sergey}, booktitle = {Conference on Robot Learning (CoRL)}, pages = {1439--1456}, year = {2020}, editor = {Kaelbling, Leslie Pack and Kragic, Danica and Sugiura, Komei}, volume = {100}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR}, url = {https://proceedings.mlr.press/v100/veerapaneni20a}, }

2018

Tricking Neural Networks: Create Your Own Adversarial Examples

Daniel Geng , and Rishi Veerapaneni

Machine Learning @ Berkeley, 2018

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This article explains what adversarial examples are and how to create your different types of adversarial examples for neural networks.
@article{geng2018adversarial_examples, title = {Tricking Neural Networks: Create Your Own Adversarial Examples}, author = {Geng, Daniel and Veerapaneni, Rishi}, journal = {Machine Learning @ Berkeley}, year = {2018}, }

2017

Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis

Assaf Hoogi , Arjun Subramaniam* , Rishi Veerapaneni*, and Daniel L. Rubin

IEEE Transactions on Medical Imaging, 2017

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We propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNN-based and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p <;0.001, Wilcoxon).
@article{hoogi2017adaptive_segmentation, author = {Hoogi, Assaf and Subramaniam*, Arjun and Veerapaneni*, Rishi and Rubin, Daniel L.}, journal = {IEEE Transactions on Medical Imaging}, title = {Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis}, year = {2017}, volume = {36}, number = {3}, pages = {781-791}, keywords = {Lesions;Level set;Image segmentation;Active contours;Adaptation models;Adaptive estimation;Neural networks;Active contours;adaptive parameters;convolutional neural network;image segmentation}, doi = {10.1109/TMI.2016.2628084}, }