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Imon Banerjee

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TMLR Journal 2026 Journal Article

Adaptive Model Selection in Offline Contextual MDP's without Stationarity

  • Riddhiman Bhattacharya
  • Sayak Chakrabarty
  • Imon Banerjee

Contextual MDP's are powerful tools with wide applicability in areas from biostatistics to machine learning. However, specializing them to offline datasets has been challenging due to a lack of robust, theoretically backed, methods. Our work tackles this problem by introducing a new approach towards adaptive estimation and cost optimization of contextual MDP's. This estimator, to the best of our knowledge, is the first of its kind, and is endowed with strong optimality guarantees. We achieve this by overcoming the key technical challenges evolving from the endogenous properties of contextual MDP's; such as non-stationarity, or model irregularity. Our guarantees are established under complete generality by utilizing the relatively recent and powerful statistical technique of $T$-estimation (Baraud, 2011). We first provide a procedure for selecting an estimator given a sample from a contextual MDP and use it derive oracle risk bounds under two distinct, but nevertheless meaningful, loss functions. We then consider the problem of determining the optimal control with the aid of the aforementioned density estimate and provide finite sample guarantees for the cost function.

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NeurIPS Conference 2025 Conference Paper

Small Resamples, Sharp Guarantees: Convergence Rates for Resampled Studentized Quantile Estimators

  • Imon Banerjee
  • Sayak Chakrabarty

The m-out-of-n bootstrap—proposed by \cite{bickel1992resampling}—approximates the distribution of a statistic by repeatedly drawing $m$ subsamples ($m \ll n$) without replacement from an original sample of size n; it is now routinely used for robust inference with heavy-tailed data, bandwidth selection, and other large-sample applications. Despite this broad applicability across econometrics, biostatistics, and machine-learning workflows, rigorous parameter-free guarantees for the soundness of the m-out-of-n bootstrap when estimating sample quantiles have remained elusive. This paper establishes such guarantees by analysing the estimator of sample quantiles obtained from m-out-of-n resampling of a dataset of length n. We first prove a central limit theorem for a fully data-driven version of the estimator that holds under a mild moment condition and involves no unknown nuisance parameters. We then show that the moment assumption is essentially tight by constructing a counter-example in which the CLT fails. Strengthening the assumptions slightly, we derive an Edgeworth expansion that delivers exact convergence rates and, as a corollary, a Berry–Esséen bound on the bootstrap approximation error. Finally, we illustrate the scope of our results by obtaining parameter-free asymptotic distributions for practical statistics, including the quantiles for random walk MH, and rewards of ergodic MDP's, thereby demonstrating the usefulness of our theory in modern estimation and learning tasks.

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JBHI Journal 2025 Journal Article

Unsupervised Hybrid framework for ANomaly Detection (HAND)- applied to Screening Mammogram

  • Zhemin Zhang
  • Bhavika Patel
  • Bhavik Patel
  • Imon Banerjee

Out-of-distribution (OOD) detection is essential for improving the generalization of of AI models used in mammogram screening, as unknown distribution shifts in external datasets can degrade performance. Identifying OOD samples helps maintain strong model performance across internal external datasets. Given the challenge of limited prior knowledge about OOD samples in external datasets, unsupervised generative learning is a preferable solution which trains the model to discern the normal characteristics of in-distribution (ID) data. Generic hypothesis is that during inference, the model aims to reconstruct ID samples accurately, while OOD samples exhibit poorer reconstruction due to their divergence from normality. Inspired by the hybrid architectures combining CNNs and trans formers, we developed a novel backbone- HAND, for detecting OOD from large-scale digital screening mammogram studies. However, relying solely on reconstruction error is insufficient for reliable OOD detection, as it may not consistently distinguish between ID and OOD samples. To address this, we incorporated synthetic OOD samples and a parallel discriminator in the latent space to distinguish between ID and OOD samples. Additionally, we applied gradient reversal to the OOD reconstruction loss, effectively discouraging the model from accurately reconstructing OODinputs and reinforcing its ability to differentiate them from ID samples. An anomaly score is computed by weighting the reconstruction and discriminator loss. On internal RSNA mammogram held-out test and external Mayo clinic hand-curated dataset, the proposed HAND model outperformed encoder-based and GAN-based baselines, and interestingly, it also outperformed the hybrid CNN+transformer baselines. Therefore, the proposed HAND pipeline offers an automated efficient computational solution for domain-specific quality checks in external screening mammograms, yielding actionable insights without direct expo sure to the private medical imaging data.

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JBHI Journal 2023 Journal Article

MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation

  • Xiaoyuan Guo
  • Judy Wawira Gichoya
  • Hari Trivedi
  • Saptarshi Purkayastha
  • Imon Banerjee

Automated curation of noisy external data in the medical domain has long been in high demand, as AI technologies need to be validated using various sources with clean, annotated data. Identifying the variance between internal and external sources is a fundamental step in curating a high-quality dataset, as the data distributions from different sources can vary significantly and subsequently affect the performance of AI models. The primary challenges for detecting data shifts are - (1) accessing private data across healthcare institutions for manual detection and (2) the lack of automated approaches to learn efficient shift-data representation without training samples. To overcome these problems, we propose an automated pipeline called MedShift to detect top-level shift samples and evaluate the significance of shift data without sharing data between internal and external organizations. MedShift employs unsupervised anomaly detectors to learn the internal distribution and identify samples showing significant shiftness for external datasets, and then compares their performance. To quantify the effects of detected shift data, we train a multi-class classifier that learns internal domain knowledge and evaluates the classification performance for each class in external domains after dropping the shift data. We also propose a data quality metric to quantify the dissimilarity between internal and external datasets. We verify the efficacy of MedShift using musculoskeletal radiographs (MURA) and chest X-ray datasets from multiple external sources. Our experiments show that our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently.

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JBHI Journal 2023 Journal Article

Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks

  • Siyi Tang
  • Amara Tariq
  • Jared A. Dunnmon
  • Umesh Sharma
  • Praneetha Elugunti
  • Daniel L. Rubin
  • Bhavik N. Patel
  • Imon Banerjee

Reduction in 30-day readmission rate is an important quality factor for hospitals as it can reduce the overall cost of care and improve patient post-discharge outcomes. While deep-learning-based studies have shown promising empirical results, several limitations exist in prior models for hospital readmission prediction, such as: (a) only patients with certain conditions are considered, (b) do not leverage data temporality, (c) individual admissions are assumed independent of each other, which ignores patient similarity, (d) limited to single modality or single center data. In this study, we propose a multimodal, spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission, which fuses in-patient multimodal, longitudinal data and models patient similarity using a graph. Using longitudinal chest radiographs and electronic health records from two independent centers, we show that MM-STGNN achieved an area under the receiver operating characteristic curve (AUROC) of 0. 79 on both datasets. Furthermore, MM-STGNN significantly outperformed the current clinical reference standard, LACE+ (AUROC = 0. 61), on the internal dataset. For subset populations of patients with heart disease, our model significantly outperformed baselines, such as gradient-boosting and Long Short-Term Memory models (e. g. , AUROC improved by 3. 7 points in patients with heart disease). Qualitative interpretability analysis indicated that while patients' primary diagnoses were not explicitly used to train the model, features crucial for model prediction may reflect patients' diagnoses. Our model could be utilized as an additional clinical decision aid during discharge disposition and triaging high-risk patients for closer post-discharge follow-up for potential preventive measures.

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AIIM Journal 2019 Journal Article

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

  • Imon Banerjee
  • Yuan Ling
  • Matthew C. Chen
  • Sadid A. Hasan
  • Curtis P. Langlotz
  • Nathaniel Moradzadeh
  • Brian Chapman
  • Timothy Amrhein

This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system – PEFinder and traditional machine learning methods – SVM and Adaboost. We proposed two distinct deep learning models – (i) CNN Word – Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7370 clinical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Experimental results suggest that the performance of the deep learning models that are trained on a single institutional dataset, are better than rule-based PEFinder on our multi-institutional test sets. The best F1 score for the presence of PE in an adult patient population was 0. 99 (DPA-HNN) and for a pediatrics population was 0. 99 (HNN) which shows that the deep learning models being trained on adult data, demonstrated generalizability to pediatrics population with comparable accuracy. Our work suggests feasibility of broader usage of neural network models in automated classification of multi-institutional imaging text reports for a variety of applications including evaluation of imaging utilization, imaging yield, clinical decision support tools, and as part of automated classification of large corpus for medical imaging deep learning work.

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NeurIPS Conference 2017 Conference Paper

Inferring Generative Model Structure with Static Analysis

  • Paroma Varma
  • Bryan He
  • Payal Bajaj
  • Nishith Khandwala
  • Imon Banerjee
  • Daniel Rubin
  • Christopher Ré

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels. We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n-th degree relations. Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3. 81 F1 points. Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3. 07 accuracy points when heuristics are used to label radiology data without ground truth labels.

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