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Found 17 papers, 7 papers with code
Group Distributionally Robust Knowledge Distillation
Knowledge distillation enables fast and effective transfer of features learned from a bigger model to a smaller one.
Compositional Representation Learning for Brain Tumour Segmentation
For brain tumour segmentation, deep learning models can achieve human expert-level performance given a large amount of data and pixel-level annotations.
Automated clinical coding using off-the-shelf large language models
The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders.
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
Automated approaches to radiology reporting require the image to be encoded into a suitable token representation for input to the language model.
Compositionally Equivariant Representation Learning
By modelling the compositional representations with learnable von-Mises-Fisher (vMF) kernels, we explore how different design and learning biases can be used to enforce the representations to be more compositionally equivariant under un-, weakly-, and semi-supervised settings.
The role of noise in denoising models for anomaly detection in medical images
Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
Maximisation of mutual information is achieved by introducing an auxiliary network and training with a latent regression loss.
What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
This requires training with healthy and unhealthy data in DPMs.
vMFNet: Compositionality Meets Domain-generalised Segmentation
Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image.
Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers
We use the indication field to drive better image classification, by taking a transformer network which is unimodally pre-trained on text (BERT) and fine-tuning it for multimodal classification of a dual image-text input.
Learning Disentangled Representations in the Imaging Domain
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision.
Survey: Leakage and Privacy at Inference Time
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data.
INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
We evaluate the method on two datasets: a new CLEVR-Seg dataset where we segment objects based on location, and the ACDC dataset conditioned on cardiac phase and slice location within the volume.
Paying Per-label Attention for Multi-label Extraction from Radiology Reports
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain.
Teacher-Student chain for efficient semi-supervised histology image classification
Deep learning shows great potential for the domain of digital pathology.
Language Transfer for Early Warning of Epidemics from Social Media
However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages.
Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data
We present an efficient neural network method for locating anatomical landmarks in 3D medical CT scans, using atlas location autocontext in order to learn long-range spatial context.
