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Found 14 papers, 4 papers with code
VoxelPrompt: A Vision-Language Agent for Grounded Medical Image Analysis
We present VoxelPrompt, an agent-driven vision-language framework that tackles diverse radiological tasks through joint modeling of natural language, image volumes, and analytical metrics.
Saliency Cards: A Framework to Characterize and Compare Saliency Methods
Moreover, with saliency cards, we are able to analyze the research landscape in a more structured fashion to identify opportunities for new methods and evaluation metrics for unmet user needs.
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models.
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings
We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process.
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions
We introduce visual deprojection: the task of recovering an image or video that has been collapsed along a dimension.
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications.
Ranked #1 on
Brain Image Segmentation
on T1-weighted MRI
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown.
Bolt: Accelerated Data Mining with Fast Vector Compression
We introduce a vector quantization algorithm that can compress vectors over 12x faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10x.
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data
Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world's data.
Uncovering Voice Misuse Using Symbolic Mismatch
Voice disorders affect an estimated 14 million working-aged Americans, and many more worldwide.
Transferring Knowledge from Text to Predict Disease Onset
Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem.
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task
A patient's risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient's pathophysiology over time.
Active Learning Applied to Patient-Adaptive Heartbeat Classification
While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researchers have had limited success in applying supervised machine learning to the same task.
Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch
We hypothesize that high risk patients can be identified using symbolic mismatch, as individuals in a population with unusual long-term physiological activity.
