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Found 40 papers, 8 papers with code
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations.
Large Language Models Are Partially Primed in Pronoun Interpretation
Recent psycholinguistic studies suggest that humans adapt their referential biases with recent exposure to referential patterns; closely replicating three relevant psycholinguistic experiments from Johnson & Arnold (2022) in an in-context learning (ICL) framework, we found that InstructGPT adapts its pronominal interpretations in response to the frequency of referential patterns in the local discourse, though in a limited fashion: adaptation was only observed relative to syntactic but not semantic biases.
MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks.
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation.
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation
In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation.
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.
Bridge the Gap between Language models and Tabular Understanding
For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples.
Aligning Source Visual and Target Language Domains for Unpaired Video Captioning
To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language.
Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model
We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge.
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures.
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i. e., passage and question.
Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging
Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI.
Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness
Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions.
Graph-in-Graph Network for Automatic Gene Ontology Description Generation
This task aims to automatically generate a sentence that describes the function of a GO term belonging to one of the three categories, i. e., molecular function, biological process, and cellular component.
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation.
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.
Ranked #1 on
Spoken Language Understanding
on Spoken-SQuAD
Conversational Question Answering
Spoken Language Understanding
+1
Diffeomorphic Image Registration with Neural Velocity Field
Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations.
Class-Aware Adversarial Transformers for Medical Image Segmentation
In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation.
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
KGAE consists of a pre-constructed knowledge graph, a knowledge-driven encoder and a knowledge-driven decoder.
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.
Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation
However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering
In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage.
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.
Self-supervised Dialogue Learning for Spoken Conversational Question Answering
We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence.
Undistillable: Making A Nasty Teacher That CANNOT teach students
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering
Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora.
Audio Signal Processing
Conversational Question Answering
+2
Knowledge Distillation for Improved Accuracy in Spoken Question Answering
However, the recent work shows that ASR systems generate highly noisy transcripts, which critically limit the capability of machine comprehension on the SQA task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Towards Data Distillation for End-to-end Spoken Conversational Question Answering
In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation
However, the well-trained models often fail in the target domain due to the domain shift.
Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding
While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information.
Low-Dose CT via Deep CNN with Skip Connection and Network in Network
The use of deep convolutional (Conv) neural networks for noise reduction in Low-Dose CT (LDCT) images has recently shown a great potential in this important application.
Super-resolution MRI through Deep Learning
Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.
Medical Physics
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.
