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Found 39 papers, 21 papers with code
Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Graph Variational Autoencoder with Contrastive Learning
This research provides valuable insights into the relationship between neurological disorder and hippocampal shape changes in different age groups of MS populations using a Graph VAE with Supervised Contrastive loss.
ShadowSense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection from RGB-Thermal Drone Imagery
Accurate detection of individual tree crowns from remote sensing data poses a significant challenge due to the dense nature of forest canopy and the presence of diverse environmental variations, e. g., overlapping canopies, occlusions, and varying lighting conditions.
Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images
In a quest to answer this question, we built a large-scale dataset (185538 images) with reliable measurements for Ki67, ER, PR, and HER2 statuses.
Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection
Extensive experiments demonstrate the superior performance of our approach over state-of-the-art SSOD methods.
Document Image Cleaning using Budget-Aware Black-Box Approximation
In this work, we propose two sample selection algorithms to train an OCR preprocessor with less than 10% of the original system's OCR engine queries, resulting in more than 60% reduction of the total training time without significant loss of accuracy.
Towards Early Prediction of Human iPSC Reprogramming Success
This paper presents advancements in automated early-stage prediction of the success of reprogramming human induced pluripotent stem cells (iPSCs) as a potential source for regenerative cell therapies. The minuscule success rate of iPSC-reprogramming of around $ 0. 01% $ to $ 0. 1% $ makes it labor-intensive, time-consuming, and exorbitantly expensive to generate a stable iPSC line.
Weakly Supervised Realtime Dynamic Background Subtraction
In this work, we propose a weakly supervised framework that can perform background subtraction without requiring per-pixel ground-truth labels.
Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images
Visual explanation of ``black-box'' models allows researchers in explainable artificial intelligence (XAI) to interpret the model's decisions in a human-understandable manner.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review
This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three primary perspectives: bark beetle & host interactions, RS, and ML/DL.
Unsupervised diffeomorphic cardiac image registration using parameterization of the deformation field
Finally, it is suitable for cardiac data processing, since the nature of this parameterization is to define the deformation field in terms of the radial and rotational components.
Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices
Further, a hybrid pipeline consisting of the upsampler and classifier, followed by a peak detection algorithm was developed.
Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning
Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world.
Learning-based Monocular 3D Reconstruction of Birds: A Contemporary Survey
In nature, the collective behavior of animals, such as flying birds is dominated by the interactions between individuals of the same species.
Dynamic Background Subtraction by Generative Neural Networks
One of the challenges for background subtraction methods is dynamic background, which constitute stochastic movements in some parts of the background.
Towards Positive Jacobian: Learn to Postprocess Diffeomorphic Image Registration with Matrix Exponential
We present a postprocessing layer for deformable image registration to make a registration field more diffeomorphic by encouraging Jacobians of the transformation to be positive.
A training-free recursive multiresolution framework for diffeomorphic deformable image registration
The moving image is warped successively at each resolution and finally aligned to the fixed image; this procedure is recursive in a way that at each resolution, a fully convolutional network (FCN) models a progressive change of deformation for the current warped image.
GPEX, A Framework For Interpreting Artificial Neural Networks
Using our method we find out that on 5 datasets, only a subset of those theoretical assumptions are sufficient.
Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction
On CIFAR, we reach similar accuracy to SOTA methods with 15% and 24% higher FLOPs reduction.
Unknown-box Approximation to Improve Optical Character Recognition Performance
Unlike the previous OCR agnostic preprocessing techniques, the proposed approach approximates the gradient of a particular OCR engine to train a preprocessor module.
Optical Character Recognition
Optical Character Recognition (OCR)
Locating Cephalometric X-Ray Landmarks with Foveated Pyramid Attention
For very large images, this makes training untenable, as the memory and computation required for activation maps scales quadratically with the side length of an image.
Ordinary Differential Equation and Complex Matrix Exponential for Multi-resolution Image Registration
Autograd-based software packages have recently renewed interest in image registration using homography and other geometric models by gradient descent and optimization, e. g., AirLab and DRMIME.
To Filter Prune, or to Layer Prune, That Is The Question
LayerPrune presents a set of layer pruning methods based on different criteria that achieve higher latency reduction than filter pruning methods on similar accuracy.
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance.
DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration
In this work, we present a novel unsupervised image registration algorithm.
Animal Detection in Man-made Environments
Automatic detection of animals that have strayed into human inhabited areas has important security and road safety applications.
Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter pruning
This is achieved by removing the least important features with a novel joint end-to-end filter pruning.
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning
In addition, we achieve 95. 5% and 93. 19% on CompCars on both train-test splits, 70-30 and 50-50, outperforming the other methods by 4. 5% and 8% respectively.
Ranked #1 on
Fine-Grained Image Classification
on BoxCars116K
River Ice Segmentation with Deep Learning
This paper deals with the problem of computing surface ice concentration for two different types of ice from digital images of river surface.
End-to-end Learning of Convolutional Neural Net and Dynamic Programming for Left Ventricle Segmentation
Differentiable programming is able to combine different functions or programs in a processing pipeline with the goal of applying end-to-end learning or optimization.
Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection
Thanks to the SC/CS recovery algorithm (L1 optimization) that can recover sparse cell locations from the output of CNN.
Online Illumination Invariant Moving Object Detection by Generative Neural Network
In this paper, we propose an extension of a state-of-the-art batch MOD method (ILISD) to an online/incremental MOD using unsupervised and generative neural networks, which use illumination invariant image representations.
Generative Adversarial Networks using Adaptive Convolution
Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator.
Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing
In this paper, we seek a different route and propose a convolutional neural network (CNN)-based cell detection method that uses encoding of the output pixel space.
Face Recognition using Multi-Modal Low-Rank Dictionary Learning
Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations.
Object Classification with Joint Projection and Low-rank Dictionary Learning
To address these issues, we propose a joint projection and low-rank dictionary learning method using dual graph constraints (JP-LRDL).
Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net
Next, we minimize this UB-SSD by adjusting both the parameters of the FCNN and the parameters of the deformable model in coordinate descent.
Convolutional Gated Recurrent Networks for Video Segmentation
This architecture is tested for both binary and semantic video segmentation tasks.
Recurrent Fully Convolutional Networks for Video Segmentation
Accordingly, we propose a novel method for online segmentation of video sequences that incorporates temporal data.
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints
To address this issue, we propose a joint projection and dictionary learning using low-rank regularization and graph constraints (JPDL-LR).
