Publications

2025

1. 

   Representation Learning Methods for Single-Cell Microscopy are Confounded by Background Cells

   Arushi Gupta, Alan Moses, and Alex X Lu

   Machine Learning in Computational Biology (MLCB), 2025

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   Oral presentation

   Deep learning models are widely used to extract feature representations from microscopy images. While these models are used for single-cell analyses, such as studying single-cell heterogeneity, they typically operate on image crops centered on individual cells with background information present, such as other cells, and it remains unclear to what extent the conclusions of single-cell analyses may be altered by this. In this paper, we introduce a novel evaluation framework that directly tests the robustness of crop-based models to background information. We create synthetic single-cell crops where the center cell’s localization is fixed and the background is swapped–e.g., with backgrounds from other protein localizations. We measure how different backgrounds affect localization classification performance using model-extracted features. Applying this framework to three leading models for single-cell microscopy for analyzing yeast protein localization, we find that all lack robustness to background cells. Localization classification accuracy drops by up to 15.8% when background cells differ in localization from the center cell compared to when the localization is the same. We further show that this lack of robustness can affect downstream biological analyses, such as the task of estimating proportions of cells for proteins with single-cell heterogeneity in localization. Ultimately, our framework provides a concrete way to evaluate single-cell model robustness to background information and highlights the importance of learning background-invariant features for reliable single-cell analysis.

2024

1. 

   Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment

   Arushi Gupta^*, Rafal D. Kocielnik^*, Jiayun Wang, Firdavs Nasriddinov, Cherine Yang, Elyssa Wong, Anima Anandkumar, and Andrew J. Hung

   Machine Learning for Health Symposium (PMLR), 2024

   BEST PAPER Abs arXiv

   The paper won the best paper award at 2024 Machine Learning for Health Conference.

   During surgical training, real-time feedback from trainers to trainees is important for preventing errors and enhancing long-term skill acquisition. Accurately predicting the effectiveness of this feedback, specifically whether it leads to a change in trainee behavior, is crucial for developing methods for improving surgical training and education. However, relying on human annotations to assess feedback effectiveness is laborious and prone to biases, underscoring the need for an automated, scalable, and objective method. Creating such an automated system poses challenges, as it requires an understanding of both the verbal feedback delivered by the trainer and the visual context of the real-time surgical scene. To address this, we propose a method that integrates information from transcribed verbal feedback and corresponding surgical video to predict feedback effectiveness. Our findings show that both transcribed feedback and surgical video are individually predictive of trainee behavior changes, and their combination achieves an AUROC of 0.70+/-0.02, improving prediction accuracy by up to 6.6%. Additionally, we introduce self-supervised fine-tuning as a strategy for enhancing surgical video representation learning, which is scalable and further enhances prediction performance. Our results demonstrate the potential of multi-modal learning to advance the automated assessment of surgical feedback.

2. 

   Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment

   Firdavs Nasriddinov^*, Rafal D. Kocielnik^*, Arushi Gupta, Cherine Yang, Elyssa Wong, Anima Anandkumar, and Andrew Hung

   Machine Learning for Health Symposium (PMLR), 2024

   Abs arXiv

   This work introduces the first framework for reconstructing surgical dialogue from unstructured real-world recordings, which is crucial for characterizing teaching tasks. In surgical training, the formative verbal feedback that trainers provide to trainees during live surgeries is crucial for ensuring safety, correcting behavior immediately, and facilitating long-term skill acquisition. However, analyzing and quantifying this feedback is challenging due to its unstructured and specialized nature. Automated systems are essential to manage these complexities at scale, allowing for the creation of structured datasets that enhance feedback analysis and improve surgical education. Our framework integrates voice activity detection, speaker diarization, and automated speech recognition, with a novel enhancement that 1) removes hallucinations (non-existent utterances generated during speech recognition fueled by noise in the operating room) and 2) separates speech from trainers and trainees using few-shot voice samples. These aspects are vital for reconstructing accurate surgical dialogues and understanding the roles of operating room participants. Using data from 33 real-world surgeries, we demonstrated the system’s capability to reconstruct surgical teaching dialogues and detect feedback instances effectively (F1 score of 0.79+/-0.07. Moreover, our hallucination removal step improves feedback detection performance by  14%. Evaluation on downstream clinically relevant tasks of predicting Behavioral Adjustment of trainees and classifying Technical feedback, showed performances comparable to manual annotations with F1 scores of 0.82+/-0.03 and 0.81+/-0.03 respectively. These results highlight the effectiveness of our framework in supporting clinically relevant tasks and improving over manual methods.

2023

1. 

   Greedy Learning for Memory-Efficient Self-Supervised MRI Reconstruction

   Arushi Gupta, Batu M. Ozturkler, Arda Sahiner, Tolga Ergen, Arjun D. Desai, Shreyas Vasanawala, John M. Pauly, Morteza Mardani, and Mert Pilanci

   International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2023

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   Deep learning (DL) has recently shown state-of-the-art performance for accelerated MRI reconstruction. However, supervised learning requires fully-sampled training data, and training these networks with end-to-end backpropagation requires significant memory for high-dimensional imaging. These challenges limit the use of DL in high-dimensional settings where access to fully-sampled data is unavailable. Here, we propose self-supervised greedy learning for memory-efficient MRI reconstruction without fully-sampled data. The method divides the end-to-end network into smaller network modules and independently calculates a self-supervised loss for each subnetwork. The proposed method generalizes as well as end-to-end learning without fully-sampled data with at least 7x less memory usage.