Search Results for author: Judy Wawira Gichoya

Found 10 papers, 4 papers with code

Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics

no code implementations31 Jul 2022 Xiaoyuan Guo, Jiali Duan, C. -C. Jay Kuo, Judy Wawira Gichoya, Imon Banerjee

Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning.

Language Modelling Masked Language Modeling

Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification

no code implementations3 Feb 2022 Timothy L. Kline, Felipe Kitamura, Ian Pan, Amine M. Korchi, Neil Tenenholtz, Linda Moy, Judy Wawira Gichoya, Igor Santos, Steven Blumer, Misha Ysabel Hwang, Kim-Ann Git, Abishek Shroff, Elad Walach, George Shih, Steve Langer

The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.

Image Classification

MedShift: identifying shift data for medical dataset curation

no code implementations27 Dec 2021 Xiaoyuan Guo, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, Imon Banerjee

Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way.

CVAD: A generic medical anomaly detector based on Cascade VAE

1 code implementation29 Oct 2021 Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha, Imon Banerjee

The key issue is the granularity of OOD data in the medical domain, where intra-class OOD samples are predominant.

Medical Diagnosis OOD Detection

Margin-Aware Intra-Class Novelty Identification for Medical Images

1 code implementation31 Jul 2021 Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha, Imon Banerjee

Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem.

Anomaly Detection

Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs

no code implementations11 Jul 2020 Nazanin Mashhaditafreshi, Amara Tariq, Judy Wawira Gichoya, Imon Banerjee

The results show the internal performance of each of the 5 pathologies outperformed external performance on both of the models.

A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images

1 code implementation16 Apr 2020 Pradeeban Kathiravelu, Puneet Sharma, ASHISH SHARMA, Imon Banerjee, Hari Trivedi, Saptarshi Purkayastha, Priyanshu Sinha, Alexandre Cadrin-Chenevert, Nabile Safdar, Judy Wawira Gichoya

Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data transfer capabilities to run them on research clusters.

BIG-bench Machine Learning

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