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Found 25 papers, 6 papers with code
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network
In addition, word-level optimization based on numbers ignores the semantics of reports and medical images, and the generated reports often cannot achieve good performance.
AMLP:Adaptive Masking Lesion Patches for Self-supervised Medical Image Segmentation
The proposed strategies effectively address limitations in applying masked modeling to medical images, tailored to capturing fine lesion details vital for segmentation tasks.
MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation
In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency.
MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation
We leverage the masked patches selection strategy to choose masked patches with lesions to obtain more lesion representation information, and the adaptive masking strategy is utilized to help learn more mutual information and improve performance further.
Multi-Head Feature Pyramid Networks for Breast Mass Detection
Analysis of X-ray images is one of the main tools to diagnose breast cancer.
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience.
A Primary Frequency Control Strategy for Variable-Speed Pumped-Storage Plant in Power Generation Based on Adaptive Model Predictive Control
Variable-speed pumped-storage (VSPS) has great potential in helping solve the frequency control problem caused by low inertia, owing to its remarkable flexibility beyond conventional fixed-speed one, to make better use of which, a primary frequency control strategy based on adaptive model predictive control (AMPC) is proposed in this paper for VSPS plant in power generation.
Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty
In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems.
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training
Unlike most existing semi-supervised learning methods, adversarial training based methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions.
Associative Memories via Predictive Coding
We conclude by discussing the possible impact of this work in the neuroscience community, by showing that our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
RSG: A Simple but Effective Module for Learning Imbalanced Datasets
Imbalanced datasets widely exist in practice and area great challenge for training deep neural models with agood generalization on infrequent classes.
Ranked #17 on
Long-tail Learning
on Places-LT
Reverse Differentiation via Predictive Coding
Recent works prove that these methods can approximate BP up to a certain margin on multilayer perceptrons (MLPs), and asymptotically on any other complex model, and that zero-divergence inference learning (Z-IL), a variant of PC, is able to exactly implement BP on MLPs.
Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks
Predictive coding networks (PCNs) are an influential model for information processing in the brain.
Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks
However, there are several gaps between BP and learning in biologically plausible neuronal networks of the brain (learning in the brain, or simply BL, for short), in particular, (1) it has been unclear to date, if BP can be implemented exactly via BL, (2) there is a lack of local plasticity in BP, i. e., weight updates require information that is not locally available, while BL utilizes only locally available information, and (3)~there is a lack of autonomy in BP, i. e., some external control over the neural network is required (e. g., switching between prediction and learning stages requires changes to dynamics and synaptic plasticity rules), while BL works fully autonomously.
Reinforced Medical Report Generation with X-Linear Attention and Repetition Penalty
To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where attention mechanisms and reinforcement learning are integrated with the classic encoder-decoder architecture to enhance the performance of deep models.
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images
We first generate MRI images on limited datasets, then we trained three popular classification models to get the best model for tumor classification.
w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution
Non-local attention and feature learning by multi-scale methods are widely used to model network, which drives progress in medical image segmentation.
Efficient Medical Image Segmentation with Intermediate Supervision Mechanism
However, U-Net is mainly engaged in segmentation, and the extracted features are also targeted at segmentation location information, and the input and output are different.
Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey
Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep learning in recent years, which constitutes a series of medical tasks, such as detection of tumor markers, the outline of tumor leisures, subtypes and stages of tumors, prediction of therapeutic effect, and drug development.
Detecting Beneficial Feature Interactions for Recommender Systems
To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy.
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
That is, there is not yet a general evaluation platform for research on multi-agent intelligence.
Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards
Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i. e., playing games without extrinsic rewards but evaluated with extrinsic rewards.
Segmentation is All You Need
Region proposal mechanisms are essential for existing deep learning approaches to object detection in images.
Ranked #1 on
Head Detection
on Rebar Head
Bandwidth Reduction using Importance Weighted Pruning on Ring AllReduce
In order to save more communication bandwidth and preserve the accuracy on ring structure, which break the restrict as the node increase, we propose a new algorithm to measure the importance of gradients on large-scale cluster implementing ring all-reduce based on the size of the ratio of parameter calculation gradient to parameter value.
Diversity-Driven Extensible Hierarchical Reinforcement Learning
However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
