Self-supervised neuron segmentation with multi-agent reinforcement learning
The performance of existing supervised neuron segmentation methods is highly dependent
on the number of accurate annotations, especially when applied to large scale electron …
on the number of accurate annotations, especially when applied to large scale electron …
Rumor Detection with a novel graph neural network approach
The wide spread of rumors on social media has caused a negative impact on people's daily
life, leading to potential panic, fear, and mental health problems for the public. How to …
life, leading to potential panic, fear, and mental health problems for the public. How to …
Image Captioning in news report scenario
Image captioning strives to generate pertinent captions for specified images, situating itself
at the crossroads of Computer Vision (CV) and Natural Language Processing (NLP). This …
at the crossroads of Computer Vision (CV) and Natural Language Processing (NLP). This …
Etp: Learning transferable ecg representations via ecg-text pre-training
In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical,
non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have …
non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have …
Zeroth-order optimization meets human feedback: Provable learning via ranking oracles
In this study, we delve into an emerging optimization challenge involving a black-box
objective function that can only be gauged via a ranking oracle-a situation frequently …
objective function that can only be gauged via a ranking oracle-a situation frequently …
Bimcv-r: A landmark dataset for 3d ct text-image retrieval
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial
increase in the workload of medical professionals. To assist clinicians in their diagnostic …
increase in the workload of medical professionals. To assist clinicians in their diagnostic …
Unsupervised Domain Adaptation for EM Image Denoising with Invertible Networks
Electron microscopy (EM) image denoising is critical for visualization and subsequent
analysis. Despite the remarkable achievements of deep learning-based non-blind denoising …
analysis. Despite the remarkable achievements of deep learning-based non-blind denoising …
Immunotherapy efficacy prediction through a feature re-calibrated 2.5 D neural network
H Xu, C Li, L Zhang, Z Ding, T Lu, H Hu - Computer Methods and Programs …, 2024 - Elsevier
Background and objective Lung cancer continues to be a leading cause of cancer-related
mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for …
mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for …
TokenUnify: Scalable Autoregressive Visual Pre-training with Mixture Token Prediction
Autoregressive next-token prediction is a standard pretraining method for large-scale
language models, but its application to vision tasks is hindered by the non-sequential nature …
language models, but its application to vision tasks is hindered by the non-sequential nature …
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation
In the field of medical image compression, Implicit Neural Representation (INR) networks
have shown remarkable versatility due to their flexible compression ratios, yet they are …
have shown remarkable versatility due to their flexible compression ratios, yet they are …