Search Results for author:
Found 18 papers, 8 papers with code
Llama 3 Meets MoE: Efficient Upcycling
Scaling large language models (LLMs) significantly improves performance but comes with prohibitive computational costs.
Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models
To address this, recent approaches have incorporated human preference datasets to fine-tune T2I models or to optimize reward functions that capture these preferences.
StarCoder 2 and The Stack v2: The Next Generation
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #32 on
Code Generation
on MBPP
SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs
We asked GPT-4 to assess the matching quality of our question-answer turns given the paper's context, obtaining an average rating of 8. 7/10 on our 3K test set.
MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan
Furthermore, we take the transfer results as additional training data for fluid segmentation to prove the advantage of our model indirectly, i. e., in the task of data adaptation and augmentation.
A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation
Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data.
Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts
In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student semi-supervised paradigm.
Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)
Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics.
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Ranked #1 on
Medical Image Segmentation
on EM
(IoU metric)
ErrorNet: Learning error representations from limited data to improve vascular segmentation
In this paper, we propose a segmentation framework called ErrorNet, which learns to correct these segmentation mistakes through the repeated process of injecting systematic segmentation errors to the segmentation result based on a learned shape prior, followed by attempting to predict the injected error.
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on
Pulmonary Embolism Detection
on PE-CAD FPRED
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.
Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
We evaluated our model using 84 chest CT scans from the LIDC and 154 pathological cases from the LTRC datasets.
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Ranked #1 on
Semantic Segmentation
on AI-TOD
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT.
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence.
