123 citations · 138 across the 16 of their papers we have counts for
19 papers
How well do routinely collected demographic and clinical variables aid point-of-care lung ultrasound TB classification
Joshua M. Jansen van Vüren, Christiaan M. Geldenhuys, Devendra S. Parihar +7
We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB) screening using deep-learning…
Wrong Organ, Right Physics: Transferring Echocardiography Pretraining to Lung Ultrasound for Tuberculosis Screening
Christiaan M. Geldenhuys, Joshua M. Jansen van Vüren, Véronique Suttels +6
Lung ultrasound (LUS) is attractive for tuberculosis (TB) screening at primary-care level, but labelled cohorts are small. Echocardiography carries no such constraint, while sharin…
CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations
Yusuf Kesmen, Aniruddha Mukherjee, Yena Chang +7
Patients often have several co-occurring clinical conditions, and the findings needed to identify and disambiguate them emerge over the course of a consultation. Evaluating clinica…
MoBayes: A Modular Bayesian Framework for Separating Reasoning from Language in Conversational Clinical Decision Support
Yusuf Kesmen, Fay Elhassan, Jiayi Ma +7
Large language models (LLMs) are increasingly used for conversational clinical decision support, yet they conflate next token prediction with probabilistic decision making. We argu…
Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS
Arianna Francesconi, Zhixiang Dai, Arthur Stefano Moscheni +7
Voice-based digital biomarkers can enable scalable, non-invasive screening and monitoring of Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS). However, models train…
DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning
Julien T. T. Vignoud, Valérian Rousset, Hugo El Guedj +28
Data is often impractical to share for a range of well considered reasons, such as concerns over privacy, intellectual property, and legal constraints. This not only fragments the…
MMORE: Massive Multimodal Open RAG & Extraction
Alexandre Sallinen, Stefan Krsteski, Paul Teiletche +7
We introduce MMORE, an open-source pipeline for Massive Multimodal Open RetrievalAugmented Generation and Extraction, designed to ingest, transform, and retrieve knowledge from het…
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Shang-Chi Tsai, Yun-Nung Chen
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…
HashEvict: A Pre-Attention KV Cache Eviction Strategy using Locality-Sensitive Hashing
Minghui Liu, Tahseen Rabbani, Tony O'Halloran +5
Transformer-based large language models (LLMs) use the key-value (KV) cache to significantly accelerate inference by storing the key and value embeddings of past tokens. However, t…
TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering
Al Zadid Sultan Bin Habib, Kesheng Wang, Mary-Anne Hartley +2
Effective analysis of tabular data still poses a significant problem in deep learning, mainly because features in tabular datasets are often heterogeneous and have different levels…
Efficient Classification of Histopathology Images
Mohammad Iqbal Nouyed, Mary-Anne Hartley, Gianfranco Doretto +1
This work addresses how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. We use i…
Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
Ling Han, Hao Huang, Dustin Scheinost +2
Effective adaptation to distribution shifts in training data is pivotal for sustaining robustness in neural networks, especially when removing specific biases or outdated informati…
Towards Independence Criterion in Machine Unlearning of Features and Labels
Ling Han, Nanqing Luo, Hao Huang +2
This work delves into the complexities of machine unlearning in the face of distributional shifts, particularly focusing on the challenges posed by non-uniform feature and label re…
TimEHR: Image-based Time Series Generation for Electronic Health Records
Hojjat Karami, Mary-Anne Hartley, David Atienza +1
Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper,…
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Zeming Chen, Alejandro Hernández Cano, Angelika Romanou +17
Large language models (LLMs) can potentially democratize access to medical knowledge. While many efforts have been made to harness and improve LLMs' medical knowledge and reasoning…
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
Vinitra Swamy, Malika Satayeva, Jibril Frej +5
Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potenti…
Optimal Model Averaging: Towards Personalized Collaborative Learning
Felix Grimberg, Mary-Anne Hartley, Sai P. Karimireddy +1
In federated learning, differences in the data or objectives between the participating nodes motivate approaches to train a personalized machine learning model for each node. One s…
WAFFLE: Weighted Averaging for Personalized Federated Learning
Martin Beaussart, Felix Grimberg, Mary-Anne Hartley +1
In federated learning, model personalization can be a very effective strategy to deal with heterogeneous training data across clients. We introduce WAFFLE (Weighted Averaging For F…
IFedAvg: Interpretable Data-Interoperability for Federated Learning
David Roschewitz, Mary-Anne Hartley, Luca Corinzia +1
Recently, the ever-growing demand for privacy-oriented machine learning has motivated researchers to develop federated and decentralized learning techniques, allowing individual cl…