Browsing by Author "Assaad, Serge"

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    A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images
    Dov, David; Kovalsky, Shahar Z; Assaad, Serge; Cohen, Jonathan; Range, Danielle Elliott; Pendse, Avani A; Henao, Ricardo; Carin, Lawrence
    We consider thyroid-malignancy prediction from ultra-high-resolution whole-slide cytopathology images. We propose a deep-learning-based algorithm that is inspired by the way a cytopathologist diagnoses the slides. The algorithm identifies diagnostically relevant image regions and assigns them local malignancy scores, that in turn are incorporated into a global malignancy prediction. We discuss the relation of our deep-learning-based approach to multiple-instance learning (MIL) and describe how it deviates from classical MIL methods by the use of a supervised procedure to extract relevant regions from the whole-slide. The analysis of our algorithm further reveals a close relation to hypothesis testing, which, along with unique characteristics of thyroid cytopathology, allows us to devise an improved training strategy. We further propose an ordinal regression framework for the simultaneous prediction of thyroid malignancy and an ordered diagnostic score acting as a regularizer, which further improves the predictions of the network. Experimental results demonstrate that the proposed algorithm outperforms several competing methods, achieving performance comparable to human experts.
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    Hölder Bounds for Sensitivity Analysis in Causal Reasoning.
    (CoRR, 2021) Assaad, Serge; Zeng, Shuxi; Pfister, Henry; Li, Fan; Carin, Lawrence
    We examine interval estimation of the effect of a treatment T on an outcome Y given the existence of an unobserved confounder U. Using H\"older's inequality, we derive a set of bounds on the confounding bias |E[Y|T=t]-E[Y|do(T=t)]| based on the degree of unmeasured confounding (i.e., the strength of the connection U->T, and the strength of U->Y). These bounds are tight either when U is independent of T or when U is independent of Y given T (when there is no unobserved confounding). We focus on a special case of this bound depending on the total variation distance between the distributions p(U) and p(U|T=t), as well as the maximum (over all possible values of U) deviation of the conditional expected outcome E[Y|U=u,T=t] from the average expected outcome E[Y|T=t]. We discuss possible calibration strategies for this bound to get interval estimates for treatment effects, and experimentally validate the bound using synthetic and semi-synthetic datasets.
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    Principled Deep Learning for Healthcare Applications
    (2023) Assaad, Serge

    Healthcare stands to benefit from the advent of deep learning on account of (i) the massive amounts of data generated by the health system and (ii) the ability of deep models to make predictions from complex inputs. This dissertation centers on two applications of deep learning to challenging problems in healthcare.

    First, we discuss deep learning for treatment effect/counterfactual estimation in the observational setting, i.e., where the treatment assignment is not randomized (Chapters 2 and 3). For example, we may want to know the causal effect of a drug on a patient's blood pressure. We combine deep learning with classical weighting techniques to estimate average and conditional average treatment effects from observational data. We show theoretical properties of our method, including guarantees about when "balance" can be achieved between treatment groups. We then weaken the typical "ignorability" assumption and generate treatment effect intervals (instead of point-estimates).

    Second, we explore the use of deep learning applied to a difficult problem in medical imaging: classifying malignancy from thyroid cytopathology slides (Chapters 4, 5, and 6). The difficulty of this problem arises from the image size, which is typically on the order of tens of gigabytes (i.e., around 3 to 4 orders of magnitude larger than image sizes in popular deep learning architectures). Our approach is a two-step process: (i) automatically finding image regions containing follicular cell groups, (ii) classifying each region and aggregating the predictions. We show that our system works well for mobile phone images of thyroid biopsy slides, and that our system compares favorably with state-of-the-art genetic testing for malignancy.

    Finally, after my Ph.D. I plan to enter a career in autonomous driving. As an "epilogue" of this dissertation (Chapter 7), we present a method to make deep learning point-cloud models for autonomous driving which are invariant (or equivariant) to rotations. Intuitively, this is an important requirement -- a rotated bicycle should still be classified as a bicycle, and driving behavior should be independent of direction of travel. However, most deep learning models used in autonomous driving today do not satisfy these properties exactly. We propose a practical model (based on the Transformer architecture) to address this pitfall, and we showcase its performance on point-cloud classification and trajectory forecasting tasks.