New method uses image-level and pixel-level annotations for brain tumor segmentation.
problem Challenges in obtaining pixel-level annotations for brain tumor segmentation.
method Proposes a learning-based framework that combines both pixel- and image-level annotations.
result Method's performance in segmentation quality is competitive with traditional fully-supervised approach.
Paper tackles rDR classification and lesion segmentation using self-supervised equivariant learning and attention-based MIL.
problem Classifying rDR and segmenting lesions from image-level labels.
method Integrates self-supervised equivariant attention mechanism (SEAM) with attention-based multi-instance learning (MIL).
result Achieved AU ROC of 0.958 on Eyepacs dataset, outperforming state-of-the-art.
Detects object edges and assigns class labels without pixel-level annotations.
problem Semantic boundary and edge detection with image-level labels.
method Proposes a novel strategy to perform edge detection and class assignment using whole image neural nets and backpropagation.
result High pixel-wise scores indicate semantic boundary locations, suggesting edge labels are not needed during training.
Paper proposes a pipeline for weakly supervised learning in object detection and segmentation.
problem Challenges in weakly supervised learning for object detection and segmentation with only image-level labels.
method Curriculum learning pipeline with object localization, filtering, fusing, and pixel labeling.
result State-of-the-art results in multi-label image classification and weakly supervised object detection.
Approach for training deep nets with unlabeled patches.
problem Training deep neural networks with detailed expert annotations.
method Cluster-based learning from weakly labeled bags in latent space.
result Improved performance on Camelyon dataset.
This research shows unsupervised GANs can perform object segmentation without labels.
problem Performing object segmentation without pixel or image-level labels.
method Used large-scale unsupervised GAN models to differentiate foreground from background.
result Demonstrated high-quality saliency masks and new state-of-the-art performance.
Proposes a strategy to train models with minimal labeled data.
problem Scarce and expensive labeled data for medical tasks.
method Recursive training strategy to use image-level annotations for pixel-level segmentation.
result Improved segmentation of intracranial hemorrhage in CT scans.
FCN improves lidar cloud detection accuracy.
problem Segmenting lidar imagery into cloud locations.
method Semi-supervised learning with pre-training and fully supervised learning.
result FCN achieves higher cloud identification accuracy.
Paper proposes a method to generate instance labels from weakly supervised data.
problem Weakly supervised instance labeling in medical image analysis.
method Uses multiple instance learning (MIL) and knowledge distillation to generate instance-level predictions.
result Significantly outperforms state-of-the-art MIL methods in instance-level prediction.
Proposes a new neural network for better image segmentation.
problem Semantic segmentation of images.
method Capsule-based neural network with recursive traceback pipeline.
result Significantly enhances segmentation performance compared to FCN variants.
OLALA automates document layout annotation by selecting ambiguous regions for labeling.
problem Efficiently annotating complex document layouts with limited resources.
method Object-Level Active Learning framework that selects ambiguous regions for labeling and uses semi-automatic correction.
result OLALA significantly boosts model performance and improves annotation efficiency.
Bayesian CNN improves MRI stroke diagnosis accuracy and uncertainty quantification.
problem Uncertainty quantification in automated image analysis for medical decision-making.
method Bayesian Convolutional Neural Network (CNN) with aggregation methods for patient-level diagnoses.
result Bayesian CNN achieved 95.33% accuracy on 511 patients, 2% higher than non-Bayesian.
Neural network classifies breast cancer lesions using global and local image features.
problem Classifying breast cancer lesions in medical images with high resolution and small regions of interest.
method Proposes a neural network that combines global saliency maps and local patches for pixel-level saliency maps.
result Achieves radiologist-level performance in screening mammography interpretation.
A two-layer network corrects logits to defend adversarial attacks.
problem Detecting and defending adversarial attacks on deep learning models.
method A two-layer network trained on mixed logits to recover original predictions.
result Shows promising results in defense with high accuracy and interpretability.
SI learning is effective for unbalanced data, showing resilience to object dependencies.
problem Learning from unbalanced data in multi-instance learning.
method Analysis of SI learning objective with rich classifier families, focusing on unbalanced data.
result Unbalanced data improves resilience of SI method to object dependencies, especially in neural networks.
Model removes objects from general scenes using weak supervision.
problem Automatic object removal from general scene images with weak supervision.
method Two-stage editor architecture with mask generator and image in-painter; novel GAN prior for mask generator.
result Effectively removes a wide variety of objects from general scenes using weak supervision.
Weakly supervised learning for histopathology disease localization.
problem Locating abnormal cells or single cells in histopathology slides.
method Pre-trained deep convolutional networks, feature embedding, top instances and negative evidence.
result Comparable performance to strong annotations on lymph node metastases detection challenge.
ProbKT uses probabilistic logical reasoning to train object detection models with weak supervision.
problem Training object detection models requires instance-level annotations, which are often unavailable.
method ProbKT, a framework based on probabilistic logical reasoning, uses arbitrary types of weak supervision.
result ProbKT leads to significant improvement and better generalization compared to existing baselines.
Deep learning classifies OCT images of normal vs AMD with high accuracy.
problem Automated classification of OCT images for diagnosing Age-related Macular Degeneration.
method Automated extraction of OCT images linked to clinical data, training a deep neural network.
result Deep learning achieved high accuracy (92.64% sensitivity, 93.69% specificity) in classifying OCT images of normal vs AMD.
Study uses image analysis to predict MSI status in tumors.
problem Challenges in distinguishing MSI from its counterpart.
method Interpretable pathological image analysis strategies using Haematoxylin and eosin-stained images.
result Strategies achieve decent performance in MSI prediction.
This work improves medical image segmentation with limited annotations using contrastive learning.
problem Lack of labeled data for medical image segmentation.
method Contrastive learning framework for semi-supervised segmentation with domain-specific and problem-specific cues.
result Significant improvements in segmentation performance compared to other methods.
ProtoPNet uses deep learning to classify images by identifying prototypical parts.
problem Challenging image classification tasks where understanding reasoning is important.
method ProtoPNet architecture that reasons by finding prototypical parts and combining evidence.
result ProtoPNet achieves comparable accuracy to non-interpretable models and provides interpretability.
Dual-edge spatial Jacobian image graph for interpretable diabetic retinopathy grading
problem Automated diabetic retinopathy grading from color fundus photographs
method Dual-edge spatial-Jacobian image graph
result 0.8076 accuracy, 0.8312 quadratic weighted kappa, 0.5915 macro-F1, 0.9330 adjacent-grade accuracy
Deep learning model uses mixed supervision for brain tumor segmentation.
problem Costly manual tumor segmentation data.
method Extends segmentation networks with an image-level classification branch.
result Significant improvement in segmentation performance.
Deep learning improves breast cancer detection on mammograms.
problem Improving accuracy of breast cancer detection in mammograms.
method End-to-end deep learning approach using convolutional networks.
result Deep learning method achieved high accuracy on various mammography datasets.
In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and …
A new method combines OCSVM with representation learning for UAD.
problem Detect anomalies without labeled data, especially in rare or unavailable cases.
method Custom loss formulation that aligns latent features with OCSVM decision boundary.
result Succeeds in detecting small, non-hyperintense lesions in MRI.
Improved breast cancer screening with a fast, memory-efficient model.
problem Classifying high-resolution breast cancer screening images.
method Extends globally-aware multiple instance classifier to handle image-level labels.
result Achieves AUC of 0.93 in classifying malignant findings, outperforming existing methods.
Paper explores generalization of GAN image forensics methods.
problem Ensuring forensics models detect GAN-generated images across new types.
method Preprocessed images training for a forensic CNN model.
result Proposed method effectively detects GAN-generated images.
A new approach to unsupervised learning using recognition-parametrised models.
problem Discovering meaningful latent structure in observational data.
method Recognition-Parametrised Model (RPM) combining parametric and non-parametric components.
result Effective learning of latent structure without explicit generative models.
New method reduces false positives in weakly supervised pixel-level localization.
problem Reduces false positives in weakly supervised pixel-level localization.
method Proposes a deep learning method using conditional entropy to constrain the localizer.
result Significant improvements in image-level classification and pixel-level localization.
PACE explains ViTs by modeling patch-level concept distributions, surpassing existing methods.
problem Lack of trustworthy post-hoc explanations for Vision Transformers (ViTs)
method Variational Bayesian explanation framework (PACE)
result PACE surpasses state-of-the-art methods in meeting desiderata for ViT explanations.
Convolutional DGP models improve image classification performance.
problem Applying deep Gaussian processes to computer vision tasks.
method Developed Convolutional DGP models using convolution kernels.
result Outperforms strong GP baselines on multi-class image classification.
Method learns feature maps from deep CNN layers for weakly supervised chest pathology localization.
problem Localization of chest pathologies in X-ray images is challenging due to varying sizes and appearances.
method Class-aware deep multiscale feature learning using intermediate feature maps from CNN layers.
result Improves localization performance of small pathologies like nodules and masses.
RCAV quantifies model sensitivity to semantic concepts, improving interpretability methods.
problem Lack of semantic interpretability in image classification models.
method RCAV calculates concept gradients and ascent steps to assess model sensitivity to semantic concepts.
result RCAV yields more accurate and robust interpretations of model behavior.
Proposes a new method for robust uncertainty quantification in regression tasks.
problem Robust uncertainty estimation for deep neural networks in regression tasks.
method Generalized Auxiliary Uncertainty Estimator (AuxUE) scheme, considering both aleatoric and epistemic uncertainties.
result DIDO method provides robust uncertainty estimates in noisy inputs, scalable to image-level and pixel-wise tasks.
Proposes a new model for noisy labels considering multiple labelers and adversarial attacks.
problem Real-world noisy label models with multiple labelers and adversarial attacks.
method Labeler-dependent noise model with adversarial attack vectors.
result State-of-the-art approaches for learning from noisy labels are defeated by adversarial label attacks.
Label smoothing improves model performance even with noisy labels.
problem Mitigating label noise in deep learning models.
method Examined label smoothing as a technique to cope with label noise and compared it to loss-correction methods.
result Label smoothing is competitive with loss-correction techniques under label noise and beneficial for distillation from noisy data.
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
CbMLC improves multi-label classification with noisy labels.
problem Evaluating multi-label classifiers with noisy labels.
method Context-Based Multi-Label Classifier (CbMLC) that handles noisy labels without additional supervision.
result CbMLC yields substantial improvements over previous methods in noisy label settings.
LNEMLC embeds label network for multi-label classification.
problem Lack of effective adaptation and preservation of generalization abilities for unseen label combinations.
method LNEMLC embeds label network to extend input space for any base multi-label classifier.
result Statistically significant improvements over simple kNN baseline classifier.
Proposes ML-GCN for multi-label network node representation learning.
problem Complex multi-label networks with correlated labels.
method Two Siamese GCNs model node-label and label-label interactions, integrated under a unified objective function.
result Effective node representation learning with preserved label interactions.
Proposes MGPLL for PL learning with non-random noise.
problem Partial label learning with non-random label noise.
method Bi-directional mapping framework, conditional noise label generation, multi-class predictor, adversarial learning.
result Demonstrates state-of-the-art performance in partial label learning.
Logistic regression can handle noisy labels effectively when labels are imperfectly assigned by multiple experts.
problem Label noise in supervised classification due to manual labelling by multiple experts.
method Using approximate posterior probabilities of class membership from multiple experts to train logistic regression models.
result Logistic regression can be robust to label noise when classification difficulty is the only source of errors.
A new method learns label correlations for better multi-label predictions.
problem Label correlations not accurately characterized by existing approaches.
method Sparse reconstruction in the label space to learn correlations, then integrate into model training.
result Our approach outperforms state-of-the-art multi-label learning methods.
Paper tackles multi-label zero-shot learning, improving label embedding projection for unseen classes.
problem Challenges in transferring knowledge from seen to unseen classes in multi-label zero-shot learning.
method Proposes a transfer-aware embedding projection approach to project label embeddings into a low-dimensional space for better inter-label relationships and explicit information transfer.
result Demonstrates the efficacy of the proposed approach through experiments on zero-shot multi-label image classification.
FLAME auto-labels mobile data efficiently on diverse processors.
problem Accurately and efficiently labeling mobile data with unknown labels on heterogeneous processors.
method Self-adaptive auto-labeling system Flame that schedules and executes workloads on mobile processors.
result Flame achieves high labeling accuracy and performance on heterogeneous mobile processors.
PML-LFC improves PML by estimating label confidence from both feature and label spaces.
problem PML challenges in real-world scenarios where only some labels are relevant.
method PML-LFC estimates label confidence using feature and label space similarities, training a predictor with these values.
result PML-LFC achieves superior performance on synthetic and real-world datasets.