Search Results for author:
Found 46 papers, 16 papers with code
IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence
Few-shot segmentation techniques reduce the required number of images to learn to segment a new class, but careful annotations of object boundaries are still required.
Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection
Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients.
Network Inversion of Binarised Neural Nets
While the deployment of neural networks, yielding impressive results, becomes more prevalent in various applications, their interpretability and understanding remain a critical challenge.
Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications
One approach involves converting images into nodes by identifying significant keypoints within them.
Classification of Various Types of Damages in Honeycomb Composite Sandwich Structures using Guided Wave Structural Health Monitoring
We believe that we are the first to report numerical models for four types of damages in HCSS, which is followed up with experimental validation.
Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification
We propose a method to train DNNs for instance segmentation and classification on multiple datasets where the set of classes across the datasets are related but not the same.
Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination
This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images.
Transforming Breast Cancer Diagnosis: Towards Real-Time Ultrasound to Mammogram Conversion for Cost-Effective Diagnosis
Then, we utilize generative adversarial networks (GANs) to tackle the inverse problem of generating mammogram-quality images from ultrasound images.
Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data
We propose a novel approach based on reverse knowledge distillation to train large models with limited data while preventing overfitting.
Ranked #1 on
Image Registration
on FIRE
Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis
The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection.
WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting
The current state-of-the-art models for image inpainting are computationally heavy as they are based on transformer or CNN backbones that are trained in adversarial or diffusion settings.
Ranked #1 on
Image Inpainting
on ImageNet
WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution
We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture which uses a 2D-discrete wavelet transform for spatial token-mixing.
Ranked #1 on
Image Super-Resolution
on BSD100 - 2x upscaling
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation
We propose a new technique called CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation.
Ranked #1 on
Unsupervised Domain Adaptation
on FHIST
Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring
The outlier score derived from the OOD detector is used to modulate sample selection for the subsequent semi-SL stage, ensuring that samples conforming to the distribution of the few labeled samples are more frequently exposed to the subsequent semi-SL framework.
Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers
Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images.
Ranked #1 on
Image Classification
on BreakHis
Breast Cancer Histology Image Classification
Image Classification
Artificial Intelligence-based Eosinophil Counting in Gastrointestinal Biopsies
In this study, we trained and tested a deep neural network based on UNet to detect and count eosinophils in GI tract biopsies.
Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade.
EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning
With our pipeline, we achieved an average area under the curve (AUC) of 0. 964 for tumor detection, and 0. 942 for histological subtyping between adenocarcinoma and squamous cell carcinoma on the TCGA dataset.
Shallow Water Bathymetry Survey using an Autonomous Surface Vehicle
We discuss the adaptation of equipment and sensors for the collection of navigation, control, and bathymetry data and also give an overview of the vehicle setup.
Deriving Surface Resistivity from Polarimetric SAR Data Using Dual-Input UNet
DI-UNet uses a deep learning architecture to predict the resistivity using full polarimetric SAR data by promising a quick survey addition to the traditional method.
WaveMix: A Resource-efficient Neural Network for Image Analysis
The whole architecture is a stack of self-similar and resolution-preserving WaveMix blocks, which allows architectural flexibility for various tasks and levels of resource availability.
Ranked #1 on
Image Classification
on Galaxy10 DECals
(using extra training data)
Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation
Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work.
WaveMix: Resource-efficient Token Mixing for Images
The multi-scale nature of the DWT also reduces the requirement for a deeper architecture compared to the CNNs, as the latter relies on pooling for partial spatial mixing.
Ranked #14 on
Image Classification
on MNIST
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network
We present WSSAMNet, a weakly supervised method for medical image registration.
Convolutional Xformers for Vision
Vision transformers (ViTs) have found only limited practical use in processing images, in spite of their state-of-the-art accuracy on certain benchmarks.
Ranked #22 on
Image Classification
on Tiny ImageNet Classification
Perceptual cGAN for MRI Super-resolution
Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients.
Memory Efficient Adaptive Attention For Multiple Domain Learning
Training CNNs from scratch on new domains typically demands large numbers of labeled images and computations, which is not suitable for low-power hardware.
WaveMix: Multi-Resolution Token Mixing for Images
Our work suggests that research on model structures that exploit the right inductive bias is far from over, and that such models can enable the training of computer vision models in settings with limited GPU resources.
Ranked #203 on
Image Classification
on CIFAR-10
Vision Xformers: Efficient Attention for Image Classification
Secondly, we introduced an inductive bias for images by replacing the initial linear embedding layer by convolutional layers in ViX, which significantly increased classification accuracy without increasing the model size.
Ranked #206 on
Image Classification
on CIFAR-10
(using extra training data)
Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images
Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different.
PAL : Pretext-based Active Learning
To counter the paucity of data, we also deploy another head on the scoring network for regularization via multi-task learning and use an unusual self-balancing hybrid scoring function.
Activation Functions: Do They Represent A Trade-Off Between Modular Nature of Neural Networks And Task Performance
Current research suggests that the key factors in designing neural network architectures involve choosing number of filters for every convolution layer, number of hidden neurons for every fully connected layer, dropout and pruning.
Switching Loss for Generalized Nucleus Detection in Histopathology
Remarkably, without retraining on target datasets, our pre-trained nucleus detector also outperformed existing nucleus detectors that were trained on at least some of the images from the target datasets.
Visualization for Histopathology Images using Graph Convolutional Neural Networks
With the increase in the use of deep learning for computer-aided diagnosis in medical images, the criticism of the black-box nature of the deep learning models is also on the rise.
Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis
Deep neural networks have revolutionized medical image analysis and disease diagnosis.
Image-based phenotyping of diverse Rice (Oryza Sativa L.) Genotypes
The present investigation is carried out for discriminating drought tolerant, and susceptible genotypes.
Uncertainty Estimation in Cancer Survival Prediction
Survival models are used in various fields, such as the development of cancer treatment protocols.
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning
We aim to provide a better interpretation of classification results by providing localization on microscopic histopathology images.
Pixel-wise Segmentation of Right Ventricle of Heart
One of the first steps in the diagnosis of most cardiac diseases, such as pulmonary hypertension, coronary heart disease is the segmentation of ventricles from cardiac magnetic resonance (MRI) images.
Histographs: Graphs in Histopathology
Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing cancers.
Fast GPU-Enabled Color Normalization for Digital Pathology
Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology.
Some New Layer Architectures for Graph CNN
While convolutional neural networks (CNNs) have recently made great strides in supervised classification of data structured on a grid (e. g. images composed of pixel grids), in several interesting datasets, the relations between features can be better represented as a general graph instead of a regular grid.
Classification of Breast Cancer Histology using Deep Learning
In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al.
Abnormal event detection on BMTT-PETS 2017 surveillance challenge
Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames.
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+1
Deep Learning-Based Image Kernel for Inductive Transfer
We propose a method to classify images from target classes with a small number of training examples based on transfer learning from non-target classes.
