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Found 15 papers, 4 papers with code
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Foundation models in histopathology that learn general representations across a wide range of tissue types, diagnoses, and magnifications offer the potential to reduce the data, compute, and technical expertise necessary to develop task-specific deep learning models with the required level of model performance.
Discovering novel systemic biomarkers in photos of the external eye
On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7. 3-13. 2%.
Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation
For instance, Noisy Cross-Validation splits the training data into halves, and has been shown to identify low-quality labels in computer vision tasks; but it has not been applied to medical imaging tasks specifically.
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photos
An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers.
Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program
Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy.
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection.
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
Diabetic retinopathy (DR) and diabetic macular edema are common complications of diabetes which can lead to vision loss.
Scalable Annotation of Fine-Grained Categories Without Experts
We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts.
Fine-Grained Car Detection for Visual Census Estimation
In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data.
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors.
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail.
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.
Ranked #5 on
Fine-Grained Image Classification
on CUB-200-2011
(using extra training data)
Fine-Grained Recognition Without Part Annotations
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories.
ImageNet Large Scale Visual Recognition Challenge
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.
Fine-Grained Crowdsourcing for Fine-Grained Recognition
In this work, we include humans in the loop to help computers select discriminative features.
