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Found 17 papers, 11 papers with code
Effectively Fine-tune to Improve Large Multimodal Models for Radiology Report Generation
Writing radiology reports from medical images requires a high level of domain expertise.
Predicting Out-of-Distribution Error with Confidence Optimal Transport
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops.
On-the-fly Object Detection using StyleGAN with CLIP Guidance
We present a fully automated framework for building object detectors on satellite imagery without requiring any human annotation or intervention.
An Interactive Interpretability System for Breast Cancer Screening with Deep Learning
Deep learning methods, in particular convolutional neural networks, have emerged as a powerful tool in medical image computing tasks.
Wasserstein Task Embedding for Measuring Task Similarities
Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks.
Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining
To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining.
Teaching Networks to Solve Optimization Problems
In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP.
Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification
Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease.
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings
This paper focuses on non-parametric and data-independent learning from set-structured data using approximate nearest neighbor (ANN) solutions, particularly locality-sensitive hashing.
Circle Representation for Medical Object Detection
Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant.
Ranked #1 on
Medical Object Detection
on MoNuSeg 2018
Compressive Neural Representations of Volumetric Scalar Fields
We present an approach for compressing volumetric scalar fields using implicit neural representations.
SimTriplet: Simple Triplet Representation Learning with a Single GPU
The contribution of the paper is three-fold: (1) The proposed SimTriplet method takes advantage of the multi-view nature of medical images beyond self-augmentation; (2) The method maximizes both intra-sample and inter-sample similarities via triplets from positive pairs, without using negative samples; and (3) The recent mix precision training is employed to advance the training by only using a single GPU with 16GB memory.
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis
In this paper, we investigate the feasibility of aligning BiT with SimSiam.
Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation
Different from the recently proposed CutMix method, the CircleMix augmentation is optimized for the ball-shaped biomedical objects, such as glomeruli.
EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology
Inspired by the recent "human-in-the-loop" strategy, we developed EasierPath, an open-source tool to integrate human physicians and deep learning algorithms for efficient large-scale pathological image quantification as a loop.
Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images
However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI.
CircleNet: Anchor-free Detection with Circle Representation
In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus.
