Search Results for author:
Found 10 papers, 3 papers with code
Gall Bladder Cancer Detection from US Images with Only Image Level Labels
We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest.
RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis.
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining
We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1. 5% compared to SOTA.
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning
However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor.
Ranked #1 on
Gallbladder Cancer Detection
on GBCU
A Supervised Learning Approach for Robust Health Monitoring using Face Videos
Non-contact methods can have additional advantages since they are scalable with any environment where video can be captured, can be used for continuous measurements, and can be used on patients with varying levels of dexterity and independence, from people with physical impairments to infants (e. g., baby camera).
A Generic Slater-Koster Description of the Electronic Structure of Centrosymmetric Halide Perovskites
The halide perovskites have truly emerged as efficient optoelectronic materials and show the promise of exhibiting nontrivial topological phases.
Materials Science
Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution
This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30% in comparison to other common labeling strategies.
Covfefe: A Computer Vision Approach For Estimating Force Exertion
We note that the PPG signals can be obtained from the face videos, thus giving an efficient classification algorithm for the force exertion levels using face videos.
Parametric Synthesis of Text on Stylized Backgrounds using PGGANs
We describe a novel method of generating high-resolution real-world images of text where the style and textual content of the images are described parametrically.
Compressive Light Field Reconstructions using Deep Learning
We test our network reconstructions on synthetic light fields, simulated coded measurements of real light fields captured from a Lytro Illum camera, and real coded images from a custom CMOS diffractive light field camera.
