Harvard University

research

∙ 07/28/2021

Fast and Scalable Image Search For Histology

The expanding adoption of digital pathology has enabled the curation of ...

Chengkuan Chen, et al. ∙

research

∙ 04/15/2022

Learning time-dependent PDE solver using Message Passing Graph Neural Networks

One of the main challenges in solving time-dependent partial differentia...

Pourya Pilva, et al. ∙

research

∙ 01/05/2022

Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia

In recent years, deep learning has successfully been applied to automate...

Jon Braatz, et al. ∙

research

∙ 05/19/2020

Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks

Dynamic computed tomography perfusion (CTP) imaging is a promising appro...

Dufan Wu, et al. ∙

research

∙ 06/06/2022

Minimizing the Expected Posterior Entropy Yields Optimal Summary Statistics

Extracting low-dimensional summary statistics from large datasets is ess...

Till Hoffmann, et al. ∙

research

∙ 06/30/2021

Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers

Neural networks are susceptible to small transformations including 2D ro...

Spandan Madan, et al. ∙

research

∙ 06/01/2020

Cascaded Text Generation with Markov Transformers

The two dominant approaches to neural text generation are fully autoregr...

Yuntian Deng, et al. ∙

research

∙ 11/15/2018

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

Learning to predict scene depth from RGB inputs is a challenging task bo...

Vincent Casser, et al. ∙

research

∙ 06/08/2022

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines

Online reinforcement learning (RL) algorithms are increasingly used to p...

Anna L. Trella, et al. ∙

research

∙ 09/30/2019

Deep learning tools for the measurement of animal behavior in neuroscience

Recent advances in computer vision have made accurate, fast and robust m...

Mackenzie W. Mathis, et al. ∙

research

∙ 10/13/2019

Unsupervised Discovery of Sparse Multimodal Representations in High Dimensional Data

Extracting an understanding of the underlying system from high dimension...

Samuel Melton, et al. ∙

research

∙ 11/18/2019

Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

AI Safety is a major concern in many deep learning applications such as ...

Tong Che, et al. ∙

research

∙ 05/17/2021

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

In many public health settings, it is important for patients to adhere t...

Arpita Biswas, et al. ∙

research

∙ 12/13/2021

Role of Human-AI Interaction in Selective Prediction

Recent work has shown the potential benefit of selective prediction syst...

Elizabeth Bondi, et al. ∙

research

∙ 06/06/2022

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

Vision Transformers (ViTs) and their multi-scale and hierarchical variat...

Richard J. Chen, et al. ∙

research

∙ 07/02/2020

Designing Environments Conducive to Interpretable Robot Behavior

Designing robots capable of generating interpretable behavior is a prere...

Anagha Kulkarni, et al. ∙

research

∙ 12/04/2018

Towards generative adversarial networks as a new paradigm for radiology education

Medical students and radiology trainees typically view thousands of imag...

Samuel G. Finlayson, et al. ∙

research

∙ 06/18/2020

Subgraph Neural Networks

Deep learning methods for graphs achieve remarkable performance on many ...

Emily Alsentzer, et al. ∙

research

∙ 04/20/2020

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

The rapidly emerging field of computational pathology has the potential ...

Ming Y. Lu, et al. ∙

research

∙ 05/05/2020

Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is...

Dongnan Liu, et al. ∙

research

∙ 05/01/2020

Learning to Complement Humans

A rising vision for AI in the open world centers on the development of s...

Bryan Wilder, et al. ∙

research

∙ 06/18/2021

Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior

Direct reconstruction methods have been developed to estimate parametric...

Kuang Gong, et al. ∙

research

∙ 07/25/2021

Deep Learning-based Frozen Section to FFPE Translation

Frozen sectioning (FS) is the preparation method of choice for microscop...

Kutsev Bengisu Ozyoruk, et al. ∙

research

∙ 02/27/2022

Concept Graph Neural Networks for Surgical Video Understanding

We constantly integrate our knowledge and understanding of the world to ...

Yutong Ban, et al. ∙

research

∙ 12/18/2019

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

Cancer diagnosis, prognosis and therapeutic response predictions are bas...

Richard J. Chen, et al. ∙

research

∙ 11/29/2019

Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation

Fully convolutional neural networks (FCNs), and in particular U-Nets, ha...

Alireza Mehrtash, et al. ∙

research

∙ 08/04/2021

Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

The rapidly emerging field of deep learning-based computational patholog...

Richard J. Chen, et al. ∙

research

∙ 01/08/2022

Low-Rank Constraints for Fast Inference in Structured Models

Structured distributions, i.e. distributions over combinatorial spaces, ...

Justin T. Chiu, et al. ∙

research

∙ 08/05/2019

Neuroscience-inspired online unsupervised learning algorithms

Although the currently popular deep learning networks achieve unpreceden...

Cengiz Pehlevan, et al. ∙

research

∙ 07/21/2020

Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks

Solutions to differential equations are of significant scientific and en...

Dylan Randle, et al. ∙

research

∙ 02/25/2021

Towards a Unified Framework for Fair and Stable Graph Representation Learning

As the representations output by Graph Neural Networks (GNNs) are increa...

Chirag Agarwal, et al. ∙

research

∙ 03/28/2021

Gaussian Process Convolutional Dictionary Learning

Convolutional dictionary learning (CDL), the problem of estimating shift...

Andrew H. Song, et al. ∙

research

∙ 07/27/2021

Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

Cancer prognostication is a challenging task in computational pathology ...

Richard J. Chen, et al. ∙

research

∙ 06/28/2021

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Extracting structured clinical information from free-text radiology repo...

Saahil Jain, et al. ∙

research

∙ 09/27/2019

A Topological Nomenclature for 3D Shape Analysis in Connectomics

An essential task in nano-scale connectomics is the morphology analysis ...

Abhimanyu Talwar, et al. ∙

research

∙ 06/14/2020

Emergent Properties of Foveated Perceptual Systems

We introduce foveated perceptual systems, inspired by human biological s...

Arturo Deza, et al. ∙

research

∙ 12/19/2018

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

Supervised deep learning algorithms have enabled significant performance...

Bruno Lecouat, et al. ∙

research

∙ 09/08/2021

Level Set Binocular Stereo with Occlusions

Localizing stereo boundaries and predicting nearby disparities are diffi...

Jialiang Wang, et al. ∙

research

∙ 09/14/2021

Rationales for Sequential Predictions

Sequence models are a critical component of modern NLP systems, but thei...

Keyon Vafa, et al. ∙

research

∙ 10/26/2021

Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approa...

Yu-Ying Liu, et al. ∙

research

∙ 01/05/2022

Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction

Image reconstruction of low-count positron emission tomography (PET) dat...

Siqi Li, et al. ∙

research

∙ 04/06/2022

Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding

Applying methods in natural language processing on electronic health rec...

Yanjun Gao, et al. ∙

research

∙ 04/29/2022

Evolutionary Approach to Security Games with Signaling

Green Security Games have become a popular way to model scenarios involv...

Adam Żychowski, et al. ∙

research

∙ 09/25/2019

Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, ...

Ayush Singh, et al. ∙

research

∙ 12/04/2019

Deep Double Descent: Where Bigger Models and More Data Hurt

We show that a variety of modern deep learning tasks exhibit a "double-d...

Preetum Nakkiran, et al. ∙

research

∙ 03/20/2021

An Empirical Framework for Domain Generalization in Clinical Settings

Clinical machine learning models experience significantly degraded perfo...

Haoran Zhang, et al. ∙

research

∙ 04/26/2019

Learning to Prune: Speeding up Repeated Computations

It is common to encounter situations where one must solve a sequence of ...

Daniel Alabi, et al. ∙

research

∙ 04/27/2020

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Fetal cortical plate segmentation is essential in quantitative analysis ...

Haoran Dou, et al. ∙

research

∙ 09/21/2021

Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values

We investigate the fairness concerns of training a machine learning mode...

Haewon Jeong, et al. ∙

research

∙ 11/19/2021

TransMorph: Transformer for unsupervised medical image registration

In the last decade, convolutional neural networks (ConvNets) have domina...

Junyu Chen, et al. ∙

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