Search Results for author:
Found 9 papers, 6 papers with code
Multimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes
The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information.
Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency
Our experiments demonstrate that LLMs struggle in the zero-shot setting performing worse than fine-tuned specialized models in terms of F1 score, showing that this is a challenging task for LLMs.
Exploring prompts to elicit memorization in masked language model-based named entity recognition
Finally, the prompt performance of detecting model memorization is quantified by the percentage of name pairs for which the model has higher confidence for the name from the training set.
MemeGraphs: Linking Memes to Knowledge Graphs
In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture.
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients
In this paper, we propose a counterfactual solution MedSeqCF for preventing the mortality of three cohorts of ICU patients, by representing their electronic health records as medical event sequences, and generating counterfactuals by adopting and employing a text style-transfer technique.
SepLL: Separating Latent Class Labels from Weak Supervision Noise
In this work, we provide a method for learning from weak labels by separating two types of complementary information associated with the labeling functions: information related to the target label and information specific to one labeling function only.
Diagnostic Captioning: A Survey
Diagnostic Captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination.
RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams
This paper introduces RTEx, a novel methodology for a) ranking radiography exams based on their probability to contain an abnormality, b) generating abnormality tags for abnormal exams, and c) providing a diagnostic explanation in natural language for each abnormal exam.
A Survey on Biomedical Image Captioning
Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians.
