108 citations · 829 across the 38 of their papers we have counts for
7 papers · 1 filter
Diffusion Instruction Tuning
Chen Jin, Ryutaro Tanno, Amrutha Saseendran +2
We introduce Lavender, a simple supervised fine-tuning (SFT) method that boosts the performance of advanced vision-language models (VLMs) by leveraging state-of-the-art image gener…
Repairing Neural Networks by Leaving the Right Past Behind
Ryutaro Tanno, Melanie F. Pradier, Aditya Nori +1
Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases. However, such data points t…
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging
Thomas Henn, Yasukazu Sakamoto, Clément Jacquet +8
Machine learning models commonly exhibit unexpected failures post-deployment due to either data shifts or uncommon situations in the training environment. Domain experts typically…
Multi-Stage Prediction Networks for Data Harmonization
Stefano B. Blumberg, Marco Palombo, Can Son Khoo +3
In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us…
Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection
Kin Quan, Ryutaro Tanno, Michael Duong +7
Numerous lung diseases, such as idiopathic pulmonary fibrosis (IPF), exhibit dilation of the airways. Accurate measurement of dilatation enables assessment of the progression of di…
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan +2
The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy e…
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc +6
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to…