Proposes new terms for neural image compression to improve quality and efficiency.
problem Improving the quality and efficiency of neural image compression.
method Introduces a compression objective and a cycle loss term, applied to autoencoder encoder outputs, combined with reconstruction losses.
result Different autoencoders trained with varying losses produce images with distinct perceptual qualities and image-domain distortions.
MKPN predicts varying-sized kernels for burst image denoising.
problem Denoising burst images corrupted by noise.
method Deep neural network (MKPN) predicts and fuses kernels of varying sizes.
result MKPN outperforms state-of-the-art on synthetic datasets.
Study improves CNN medical image segmentation accuracy and reliability.
problem Over-confident predictions and silent failures in out-of-distribution data.
method Multi-task learning and spectral analysis of CNN feature maps.
result Joint multi-task learning models outperform dedicated models and detect OOD data more accurately.
New method generates universal adversarial perturbations across different image sources.
problem Certifying robustness of deep learning models with universal adversarial perturbations across various image sources.
method Few-shot learning approach using bilevel optimization and learning-to-optimize techniques.
result Improved attack success rate and faster performance compared to existing methods.
Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classifiers trained on images from one scanner to segment images from other scanners. We propose a transfer learning classifier that adapts to diff…
Study compares different superpixel methods for explaining CNNs in biological images.
problem Transparency of deep learning models in image classification.
method Compared Felzenszwalb, SLIC, and Compact-Watershed superpixel methods on explaining CNNs.
result Compact-Watershed method most closely matches human-referenced relevance areas.
Few-shot cell segmentation from diverse sources to target domain.
problem Efficient cell segmentation from limited annotated images.
method Meta-learning combining cross-domain tasks and invariant representation.
result Promising results from 1-10-shot learning on public databases.
An important part of Digital Pathology is the analysis of multiple digitised whole slide images from differently stained tissue sections. It is common practice to mount consecutive sections containing corresponding microscopic structures on glass slides, and to stain them differently to highlight specific tissue compon…
Improved detection of burnt areas in satellite images using evolved hyper-features.
problem Radiometric variations across satellite images and different datasets.
method Understanding feature spaces, training on multi-image datasets, evolving hyper-features, and optimizing for different classifiers.
result Training on multi-image datasets improves model generalization, and evolved hyper-features enhance classifier performance.
New techniques save bits in image compression with upsampling.
problem Lack of context dependence in current image compression methods with upsampling.
method Simple, inexpensive techniques exploiting context to predict Laplace distribution parameters.
result Average savings of 0.645 bits per difference, up to 1.489 bits.
Federated CycleGAN enables privacy-preserving image translation without central data.
problem Privacy and security issues in unsupervised image-to-image translation.
method Novel federated CycleGAN architecture that decomposes CycleGAN loss into client-specific local objectives.
result Federated CycleGAN achieves comparable performance to non-federated CycleGAN without central data exchange.
A new method for multi-label image classification using multiple feature views.
problem Limited by single-view feature, traditional matrix completion struggles with multi-label image classification.
method Multi-View Matrix Completion (MVMC) framework, combining weighted MC outputs from different views, using cross-validation for weights.
result MVMC framework improves multi-label image classification by exploiting complementary properties of different features and consistent labels.
Paper proposes a method to improve semantic segmentation for fisheye urban driving images.
problem Semantic segmentation for fisheye urban driving images is challenging due to distortion and lack of large datasets.
method A seven degrees of freedom augmentation method is proposed to transform rectilinear images into fisheye images.
result Training with seven-DoF augmentation improves model accuracy and robustness against distorted fisheye data.
Predict steering angles of self-driving cars from images.
problem Predicting steering angles for self-driving cars using image data.
method Used deep learning techniques like Transfer Learning, 3D CNN, LSTM, and ResNet to predict steering angles.
result Both models placed in the top ten of Udacity's challenge.
Transmission imaging, as an important imaging technique widely used in astronomy, medical diagnosis, and biology science, has been shown in [49] quite different from reflection imaging used in our everyday life. Understanding the structures of images (the prior information) is important for designing, testing, and choo…
A statistical algorithm for categorizing different types of matches and fraud in image databases is presented. The approach is based on a generative model of a graph representing images and connections between pairs of identities, trained using properties of a matching algorithm between images.
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different gener…
A new method models uncertainty in medical image segmentation.
problem Inherent ambiguity in anatomical structure and pathology segmentation.
method Hierarchical probabilistic model with variational autoencoder.
result Generates more realistic and diverse segmentation samples.
Paper proposes using NLPD for cGANs to improve image realism and segmentation.
problem Lack of perceptual evaluation in cGANs.
method Used NLPD to measure perceptual similarity, comparing with L1 distance.
result NLPD improves realism and segmentation accuracy in generated images.
Unified deep learning predicts Parkinson's disease from medical images.
problem Diagnosing Parkinson's disease accurately from medical images.
method Transfer learning and domain adaptation using deep convolutional and recurrent neural networks.
result The approach effectively predicts Parkinson's disease across different medical environments.
QANet estimates image segmentation quality without human inspection.
problem Quantitative evaluation of image segmentation quality.
method QANet is a Quality Assurance Network that solves a regression problem to estimate quality measures.
result QANet accurately estimates segmentation quality with respect to ground truth.
Convolutional neural networks (CNNs) have shown promising results on several segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs may degrade severely when segmenting images acquired with different scanners and/or protocols as compared to the training data, thus limiting their practical u…
A deep model learns to infer fluorescence labels from unlabeled microscopy images.
problem Challenges in obtaining high quality images of cellular structures due to complex environments and label staining limitations.
method Developed a novel deep model using global pixel transformer layers and dense blocks, incorporating multi-scale input strategy.
result Significantly outperforms state-of-the-art methods in fluorescence image prediction tasks.
We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally ch…
3D CNNs interpret brain MRI differences between men and women.
problem Interpreting 3D CNNs for voxel-wise brain MRI analysis.
method Three interpretation methods: Meaningful Perturbations, Grad CAM, and Guided Backpropagation.
result Voxel-wise 3D CNN interpretation of brain MRI data.
CURL uses neural curves to enhance global image properties.
problem Global image enhancement using neural networks.
method CURL is a multi-colour space neural retouching block trained in HSV, CIELab, and RGB color spaces.
result CURL produces state-of-the-art image quality in RGB-to-RGB and RAW-to-RGB transformations.
Paper explores vulnerabilities in image authenticity detection methods, especially printing and scanning attacks.
problem Vulnerability of image authenticity detection models to printing and scanning attacks.
method Demonstrates and proposes a new machine learning model to counter these attacks.
result Proposed model outperforms state-of-the-art models when trained on images from a single printer.
New method certifies images against transformations like rotations and translations.
problem Certifying robustness of images against transformations like rotations and translations.
method Randomized smoothing with three different kinds of defenses.
result Individual certificates can be obtained via statistical error bounds or efficient online inverse computation.
Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data. Reliable and interpretable results are possible only if considered with respect to suitable noise statistics. However, the noise in reconstr…
COOS-7 dataset benchmarks image classifier generalization.
problem Measuring generalization of image classifiers to out-of-sample datasets.
method Created COOS-7 dataset with varying covariate shifts; benchmarked multiple models.
result All classifiers failed to generalize to datasets with greater covariate shifts.
ADS explains object differences by quantifying and removing underlying properties.
problem Explaining differences between two object images.
method Align-Deform-Subtract (ADS) framework that uses semantic alignments and iterative quantification/removal of differences.
result ADS provides disentangled error measures explaining object differences in terms of underlying properties.
Hierarchical CNNs improve diagnosis of GI diseases from histopathological images.
problem Diagnosing GI diseases from histopathological images is challenging due to heterogeneity and shared features.
method Embedded a class hierarchy into a VGGNet to address the hierarchical structure of GI diseases.
result The hierarchical model achieved better results than a flat model for multi-category diagnosis of GI disorders.
AIR-Net adapts low-rank regularization dynamically for better image completion.
problem Fixed low-rank regularization limits adaptability to different images.
method AIR-Net uses adaptive and implicit regularization parameterized by a dynamic Laplacian matrix.
result AIR-Net enhances implicit regularization and outperforms fixed methods in non-uniform missing data scenarios.
Framework transfers limited steering angle data across multiple weather conditions.
problem Limited labeled data for diverse weather conditions in sensorimotor control.
method Teacher-student learning paradigm with image-to-image translation network.
result Framework generalizes well across multiple weather conditions using limited labels.
FrequentNet uses frequency domain basis vectors for image classification, making models more interpretable and efficient.
problem Image classification models are often complex and hard to interpret.
method FrequentNet selects filter vectors from frequency domain basis vectors instead of training them with back propagation.
result The method improves interpretability and efficiency of image classification models.
Transfer learning offers little benefit in medical imaging tasks.
problem Understanding transfer learning's impact on medical imaging tasks.
method Evaluation of transfer learning on two large-scale medical imaging tasks.
result Simple, lightweight models perform comparably to ImageNet architectures.
DPI quantifies phase differences in 1D and multidimensional signals using Riesz transform.
problem Quantifying phase differences in signals of varying dimensions.
method Riesz transform framework for harmonic analysis.
result DPI detects hypersynchronization and subtle changes in images and artworks.
Adapts deep learning models trained on simulated images for use with real images.
problem Difficulty in training deep neural networks on large amounts of experimental data.
method Adversarial domain adaptation method to mitigate domain shift between simulated and experimental image data.
result Adversarial domain adaptation successfully mitigates domain shift and improves numerical observer performance.
Adaptive object detection method synthesizes target domain images from source domain images.
problem Large domain gap between source and target domains in object detection.
method Cross-domain CutMix with adversarial learning.
result Higher accuracy in different domain settings compared to conventional methods.
Latent feature models are attractive for image modeling, since images generally contain multiple objects. However, many latent feature models ignore that objects can appear at different locations or require pre-segmentation of images. While the transformed Indian buffet process (tIBP) provides a method for modeling tra…
One of the problems on the way to successful implementation of neural networks is the quality of annotation. For instance, different annotators can annotate images in a different way and very often their decisions do not match exactly and in extreme cases are even mutually exclusive which results in noisy annotations a…
Deep learning solves imaging inverse problems without ground truth.
problem Solving imaging problems without perfect data.
method Taxonomy of deep learning approaches for imaging inverse problems.
result Trade-offs and failure modes identified for different methods.
Study reveals differences in medical image models' hidden representation refinement.
problem Understanding how intrinsic dimensionality changes in neural network hidden representations across different domains.
method Analysis of 11 natural and medical image datasets using 6 network architectures.
result Medical image models refine hidden representations earlier, suggesting differences in feature abstraction.
Deep learning automates bacterial image classification.
problem Manual bacterial classification is time-consuming and error-prone.
method ResNet-50 pre-trained CNN architecture with transfer learning.
result Average classification accuracy of 99.2%.
Study improves image-caption retrieval by quantifying feature and posterior uncertainty.
problem Improving reliability in image-caption retrieval tasks with deep learning models.
method Quantified feature and posterior uncertainty for model averaging and reliability measure in image-caption retrieval.
result Consistent improvement in retrieval performance with different datasets and architectures.
In this paper we propose a strategy for semi-supervised image classification that leverages unsupervised representation learning and co-training. The strategy, that is called CURL from Co-trained Unsupervised Representation Learning, iteratively builds two classifiers on two different views of the data. The two views c…
Most existing image denoising approaches assumed the noise to be homogeneous white Gaussian distributed with known intensity. However, in real noisy images, the noise models are usually unknown beforehand and can be much more complex. This paper addresses this problem and proposes a novel blind image denoising algorith…
Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an important source of information in remote sensing applications. The most complete format this type of imaging produces consists of complex-valued Hermitian matrices in every image coordinate and, as such, their visualization is challenging. Th…