Search Results for author:
Found 18 papers, 13 papers with code
An Image is Worth $K$ Topics: A Visual Structural Topic Model with Pretrained Image Embeddings
In this article, we introduce a visual Structural Topic Model (vSTM) that combines pretrained image embeddings with a structural topic model.
A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology
Results show that centroid-based methods, particularly the Improved Fully Convolutional Regression Network (IFCRN) method, outperform segmentation-based methods in terms of both detection accuracy and computational efficiency.
Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning
In this paper, we introduce a simple yet effective pretraining framework for large-scale MCI datasets.
UpStory: the Uppsala Storytelling dataset
Moreover, despite advances in the automatic analysis of human behaviour, no previous work has addressed the prediction of rapport in child-child dyadic interactions in educational settings.
Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection
This study aims to improve AI-based oral cancer detection using multimodal imaging and deep fusion.
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?
Contrastive learning can generate representations of multimodal images, reducing the challenging task of multimodal image registration to a monomodal one.
End-to-end Multiple Instance Learning with Gradient Accumulation
We conduct experiments on both QMNIST and Imagenette to investigate the performance and training time, and compare with the conventional memory-expensive baseline and a recent sampled-based approach.
Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learning
Performance at the bag level on real-world cytological data is similar for both methods, yet the single instance approach performs better on average.
Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations
We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across modalities, which combines deep learning to generate representations (embedding the different modalities in a common space) with classical feature extraction and bag-of-words models for efficient and reliable retrieval.
Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields
The method is fast; a 3. 4Mvoxel global rigid alignment requires approximately 40 seconds of computation, and the proposed algorithm outperforms a direct algorithm for the same task by more than three orders of magnitude.
Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment
We propose an efficient algorithm for computing MI for all discrete displacements (formalized as the cross-mutual information function (CMIF)), which is based on cross-correlation computed in the frequency domain.
Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study
We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration.
INSPIRE: Intensity and spatial information-based deformable image registration
INSPIRE exhibits excellent performance on the FIRE dataset, substantially outperforming several domain-specific methods.}
CoMIR: Contrastive Multimodal Image Representation for Registration
We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).
A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification.
Stochastic Distance Transform
We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise.
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification
In this report, we are presenting our automated prediction system for disease classification within dermoscopic images.
Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information
The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK).
