Search Results for author: Deepta Rajan

Found 12 papers, 0 papers with code

Designing Counterfactual Generators using Deep Model Inversion

no code implementations NeurIPS 2021 Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias

Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models.

Image Generation

Loss Estimators Improve Model Generalization

no code implementations5 Mar 2021 Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan, Andreas Spanias

With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate predictions but also do not generalize to data regimes where the training data provide no meaningful evidence.

Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification

no code implementations3 May 2020 Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap

Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support simultaneous prediction of multiple disease conditions.

Data Augmentation Few-Shot Learning +1

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models

no code implementations27 Apr 2020 Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh

The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous introspection of model behavior.

Decision Making Lesion Classification +1

Learn-By-Calibrating: Using Calibration as a Training Objective

no code implementations30 Oct 2019 Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan

Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks.

Prediction Intervals

Leveraging Medical Visual Question Answering with Supporting Facts

no code implementations28 May 2019 Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S. Ozcan

In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition.

Medical Visual Question Answering Multi-Task Learning +3

Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG

no code implementations5 Jan 2019 Deepta Rajan, David Beymer, Girish Narayan

Though deep neural networks have achieved unprecedented success in predictive modeling, they rely solely on discriminative models that can generalize poorly to unseen classes.

Understanding Behavior of Clinical Models under Domain Shifts

no code implementations20 Sep 2018 Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri

The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare.

Multi-Label Classification Unsupervised Domain Adaptation

A Generative Modeling Approach to Limited Channel ECG Classification

no code implementations18 Feb 2018 Deepta Rajan, Jayaraman J. Thiagarajan

Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling.

Classification Disease Prediction +3

Attend and Diagnose: Clinical Time Series Analysis using Attention Models

no code implementations10 Nov 2017 Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias

With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data.

Time Series Time Series Analysis

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