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Found 39 papers, 10 papers with code
StableMask: Refining Causal Masking in Decoder-only Transformer
The decoder-only Transformer architecture with causal masking and relative position encoding (RPE) has become the de facto choice in language modeling.
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization
Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning, wherein the weak-to-strong consistency regularization framework, popularized by FixMatch, is widely used as a benchmark scheme.
A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification
Immune repertoire classification, a typical multiple instance learning (MIL) problem, is a frontier research topic in computational biology that makes transformative contributions to new vaccines and immune therapies.
DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks
Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge.
Reweighted Mixup for Subpopulation Shift
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions.
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation
Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary.
Learning with Noisy Labels over Imbalanced Subpopulations
Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification
Multiple instance learning (MIL) is a powerful approach to classify whole slide images (WSIs) for diagnostic pathology.
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images
We first benchmark representative SSL methods for dense prediction tasks in pathology images.
ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification
We evaluate ReMix on two public datasets with two state-of-the-art MIL methods.
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction
Four different implementations of anatomy-specific learners are presented and explored on the top of our framework in two MRI reconstruction networks.
3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume
Therefore, designing an elegant and efficient vision transformer learner for dense prediction in medical volume is promising and challenging.
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning
Histology images are a natural choice for such a study.
Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification
To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification.
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning
Histology images are a natural choice for such study.
Blind deblurring for microscopic pathology images using deep learning networks
Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency.
Microscope Based HER2 Scoring System
Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem.
COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data
Results validate that the ST-ConvLSTM produces a Dice score of 83. 2%+-5. 1% and a RVD of 11. 2%+-10. 8%, both significantly outperforming (p<0. 05) other compared methods of linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes.
Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks
However, CNN in previous studies do not involve cell morphological information, and it is unknown whether morphological features can be directly modeled by CNN to classify cervical cells.
DeepPap: Deep Convolutional Networks for Cervical Cell Classification
However, the success of most traditional classification methods relies on the presence of accurate cell segmentations.
Convolutional Invasion and Expansion Networks for Tumor Growth Prediction
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics.
Self-Learning to Detect and Segment Cysts in Lung CT Images without Manual Annotation
In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data.
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans
The cost for each node of the graph is determined by the UNet probability maps.
Personalized Pancreatic Tumor Growth Prediction via Group Learning
Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor.
Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition
The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image Database
Obtaining semantic labels on a large scale radiology image database (215, 786 key images from 61, 845 unique patients) is a prerequisite yet bottleneck to train highly effective deep convolutional neural network (CNN) models for image recognition.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases
The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92. 5 $\pm$ 3. 1% to 93. 8 $\pm$ 2. 1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0. 75 $\pm$ 0. 60mm and 8. 63 $\pm$ 4. 44mm to 0. 30 $\pm$ 0. 27mm and 3. 65 $\pm$ 2. 87mm, respectively.
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.
Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT
Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment.
Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT
The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies.
Interleaved Text/Image Deep Mining on a Very Large-Scale Radiology Database
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system.
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.
Anatomy-specific classification of medical images using deep convolutional nets
We show that a data augmentation approach can help to enrich the data set and improve classification performance.
Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications
In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.
