Search Results for author: Daniel L. Rubin

Found 23 papers, 6 papers with code

TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring

no code implementations22 Jul 2022 Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer

We show that our sequential mistrust scores achieve high drift detection rates: over 90% of the streams show < 20% error for all domains.

Electroencephalogram (EEG) Seizure Detection

SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging

1 code implementation6 Jul 2021 Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin

In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning.

Federated Learning

COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs

1 code implementation17 May 2021 Vignav Ramesh, Blaine Rister, Daniel L. Rubin

Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models.

Computed Tomography (CT) Lesion Segmentation

Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation

1 code implementation2 Feb 2021 Rikiya Yamashita, Jin Long, Snikitha Banda, Jeanne Shen, Daniel L. Rubin

Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem.

Data Augmentation Domain Generalization +1

Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset

no code implementations15 Oct 2020 Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared A. Dunnmon, James Zou, Daniel L. Rubin

In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset.

Data Valuation Pneumonia Detection

Probabilistic bounds on neuron death in deep rectifier networks

no code implementations13 Jul 2020 Blaine Rister, Daniel L. Rubin

Neuron death is a complex phenomenon with implications for model trainability: the deeper the network, the lower the probability of finding a valid initialization.

Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis

no code implementations24 Aug 2019 Okyaz Eminaga, Mahmoud Abbas, Christian Kunder, Andreas M. Loening, Jeanne Shen, James D. Brooks, Curtis P. Langlotz, Daniel L. Rubin

A well-fitted PlexusNet-based model delivered comparable classification performance (AUC: 0. 963) in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models.

General Classification

Self-Attention Capsule Networks for Object Classification

no code implementations29 Apr 2019 Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin

Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task.

Classification General Classification +1

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI

no code implementations18 Mar 2019 Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk

For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.

CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss

no code implementations27 Nov 2018 Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, Daniel L. Rubin

To achieve the best results from data augmentation, our model uses the intersection-over-union (IOU) loss function, a close relative of the Dice loss.

Data Augmentation Image Segmentation +3

A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization

no code implementations15 Jun 2018 Imon Banerjee, Hailey H. Choi, Terry Desser, Daniel L. Rubin

We propose a scalable computerized approach for large-scale inference of Liver Imaging Reporting and Data System (LI-RADS) final assessment categories in narrative ultrasound (US) reports.

BIG-bench Machine Learning

Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients

no code implementations9 Jan 2018 Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel Chang, Daniel L. Rubin

We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence.

Intelligent Word Embeddings of Free-Text Radiology Reports

1 code implementation19 Nov 2017 Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin

Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared.

Word Embeddings

Inferring Generative Model Structure with Static Analysis

no code implementations NeurIPS 2017 Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, Christopher Ré

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.

A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes

no code implementations19 Mar 2017 Assaf Hoogi, John W. Lambert, Yefeng Zheng, Dorin Comaniciu, Daniel L. Rubin

We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes.

Computed Tomography (CT) Lesion Detection +2

Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment

no code implementations1 Dec 2016 Imon Banerjee, Lewis Hahn, Geoffrey Sonn, Richard Fan, Daniel L. Rubin

We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC).

Piecewise convexity of artificial neural networks

no code implementations17 Jul 2016 Blaine Rister, Daniel L. Rubin

Finally, that the network as a function of all its parameters is piecewise multi-convex, a generalization of biconvexity.

speech-recognition Speech Recognition

Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions

no code implementations12 Jun 2016 Assaf Hoogi, Christopher F. Beaulieu, Guilherme M. Cunha, Elhamy Heba, Claude B. Sirlin, Sandy Napel, Daniel L. Rubin

We compare our method to a global level set segmentation and to local framework that uses predefined fixed-size square windows.

Cannot find the paper you are looking for? You can Submit a new open access paper.