Search Results for author:
Found 40 papers, 24 papers with code
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes.
A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images
A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images.
Visual Rhythm Prediction with Feature-Aligning Network
In this paper, we propose a data-driven visual rhythm prediction method, which overcomes the previous works' deficiency that predictions are made primarily by human-crafted hard rules.
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
Pairwise Relation Learning for Semi-supervised Gland Segmentation
In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets.
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation
In this paper, we propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space.
Inter-slice Context Residual Learning for 3D Medical Image Segmentation
In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images.
A General Computational Framework to Measure the Expressiveness of Complex Networks Using a Tighter Upper Bound of Linear Regions
Inspired by the proof of this upper bound and theframework of matrix computation in Hinz & Van de Geer (2019), we propose ageneral computational approach to compute a tight upper bound of regions numberfor theoretically any network structures (e. g. DNN with all kind of skip connec-tions and residual structures).
A General Computational Framework to Measure the Expressiveness of Complex Networks using a Tight Upper Bound of Linear Regions
Inspired by the proof of this upper bound and the framework of matrix computation in \citet{hinz2019framework}, we propose a general computational approach to compute a tight upper bound of regions number for theoretically any network structures (e. g. DNN with all kind of skip connections and residual structures).
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation.
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Searching for novel molecules with desired chemical properties is crucial in drug discovery.
Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction
According to the consistency and reliability of their annotations, we divide nodules into three sets: a consistent and reliable set (CR-Set), an inconsistent set (IC-Set), and a low reliable set (LR-Set).
Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases.
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier
In this paper, we advocate bringing a wealth of 2D images like chest X-rays as compensation for the lack of 3D data, aiming to build a universal medical self-supervised representation learning framework, called UniMiSS.
How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules
We further evaluate how well the existing databases and generation models cover the chemical space in terms of #Circles.
Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer
We proved that a trained model in supervised deep learning minimizes the conditional risk for each input (Theorem 2. 1).
Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection
We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP.
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction
We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM.
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning.
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers
Besides, we further extend the clustering-guided attention from single-scale to multi-scale, which is conducive to dense prediction tasks.
Learning from partially labeled data for multi-organ and tumor segmentation
To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets.
Instance-specific Label Distribution Regularization for Learning with Label Noise
Specifically, we estimate the noisy posterior under the supervision of noisy labels, and approximate the batch-level noise transition matrix by estimating the inter-class correlation matrix with neither anchor points nor pseudo anchor points.
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training
In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multi-class and multi-label medical image classification from the point of loss distribution modeling and adversarial training.
Image Classification
Semi-supervised Medical Image Classification
Diffusion Model for Generative Image Denoising
In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model.
A Prompt Log Analysis of Text-to-Image Generation Systems
Despite the plenty of efforts to improve the generative models, there is limited work on understanding the information needs of the users of these systems at scale.
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner
Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation.
S4M: Generating Radiology Reports by A Single Model for Multiple Body Parts
In this paper, we seek to design a report generation model that is able to generate reasonable reports even given different images of various body parts.
Attention Mechanisms in Medical Image Segmentation: A Survey
This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation.
Transformer-based Annotation Bias-aware Medical Image Segmentation
This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors.
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors.
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation.
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?
Their behaviors are often distinct from average and modal human behaviors, in which case they tend to behave on the more altruistic and cooperative end of the distribution.
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
Therefore, in this paper, we introduce a \textbf{Ver}satile \textbf{Semi}-supervised framework (VerSemi) to point out a new perspective that integrates various tasks into a unified model with a broad label space, to exploit more unlabeled data for semi-supervised medical image segmentation.
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data.
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease.
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability.
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?
Acknowledging this limitation, our objective is to devise a framework capable of concurrently augmenting medical image and text data.
