no code implementations • 14 Nov 2022 • Jin Li, Deepta Rajan, Chintan Shah, Dinkar Juyal, Shreya Chakraborty, Chandan Akiti, Filip Kos, Janani Iyer, Anand Sampat, Ali Behrooz
Histopathology images are gigapixel-sized and include features and information at different resolutions.
no code implementations • 12 Jul 2022 • Vivek Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Andreas Spanias, Jayaraman J. Thiagarajan
We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers.
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.
no code implementations • 5 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.
no code implementations • MIDL 2019 • Luyao Shi, Deepta Rajan, Shafiq Abedin, Manikanta Srikar Yellapragada, David Beymer, Ehsan Dehghan
In addition to the classification loss, an attention loss was added during training to help the network focus attention on PE.
no code implementations • 3 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.
no code implementations • 27 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.
no code implementations • 30 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.
no code implementations • 5 Oct 2019 • Deepta Rajan, David Beymer, Shafiqul Abedin, Ehsan Dehghan
Pulmonary embolisms (PE) are known to be one of the leading causes for cardiac-related mortality.
no code implementations • 28 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.
no code implementations • 5 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.
no code implementations • 20 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.
no code implementations • 18 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.
no code implementations • 10 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.