9 citations · 26 across the 12 of their papers we have counts for
12 papers
GO4Align: Group Optimization for Multi-Task Alignment
Jiayi Shen, Cheems Wang, Zehao Xiao +2
This paper proposes \textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we desi…
Episodic Multi-Task Learning with Heterogeneous Neural Processes
Jiayi Shen, Xiantong Zhen, Qi +2
This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training setup. Specifically, we explore the potential of heterogeneous information a…
Self-Supervised Open-Ended Classification with Small Visual Language Models
Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Cees G. M. Snoek +2
We present Self-Context Adaptation (SeCAt), a self-supervised approach that unlocks few-shot abilities for open-ended classification with small visual language models. Our approach…
ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement
Merel de Leeuw den Bouter, Javier Lloret Pardo, Zeno Geradts +1
In high-stakes settings, Machine Learning models that can provide predictions that are interpretable for humans are crucial. This is even more true with the advent of complex deep…
X-TRA: Improving Chest X-ray Tasks with Cross-Modal Retrieval Augmentation
Tom van Sonsbeek, Marcel Worring
An important component of human analysis of medical images and their context is the ability to relate newly seen things to related instances in our memory. In this paper we mimic t…
Probabilistic Integration of Object Level Annotations in Chest X-ray Classification
Tom van Sonsbeek, Xiantong Zhen, Dwarikanath Mahapatra +1
Medical image datasets and their annotations are not growing as fast as their equivalents in the general domain. This makes translation from the newest, more data-intensive methods…
LifeLonger: A Benchmark for Continual Disease Classification
Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek +4
Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing new…
Multi-Task Neural Processes
Jiayi Shen, Xiantong Zhen, Marcel Worring +1
Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can enco…
Variational Multi-Task Learning with Gumbel-Softmax Priors
Jiayi Shen, Xiantong Zhen, Marcel Worring +1
Multi-task learning aims to explore task relatedness to improve individual tasks, which is of particular significance in the challenging scenario that only limited data is availabl…
Variational Topic Inference for Chest X-Ray Report Generation
Ivona Najdenkoska, Xiantong Zhen, Marcel Worring +1
Automating report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice. Recent work has shown that deep learning models can successf…
Variational Knowledge Distillation for Disease Classification in Chest X-Rays
Tom van Sonsbeek, Xiantong Zhen, Marcel Worring +1
Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be further improved, however, by also employing Electr…
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
Jia-Hong Huang, Chao-Han Huck Yang, Fangyu Liu +9
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and…