Jialu Cai

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Recently, some state-of-the-art (SOTA) methods have designed dedicated context extractors to capture the global information that serves as a key clue for describing crowd density. A promising alternative is the transformer-based model which inherently captures long-range context dependencies. Recent related studies have made impressive progress, yet the following issues remain: (1) The size of the heads in the image is large near and small far away. The existing models fail to cope well with these variations. (2) There is an imbalance in the distribution of samples across different densities in the dataset, which leads to poor network performance on density distributions with a small number of samples. To address these issues, we propose to aggregate multi-scale views through Compare and Focus strategies. In terms of the first strategy, we mine differential hints from multi-scale view features to capture heads of varying sizes. This can effectively reduce the influence of redundant information while perceiving the subtleties of various view inputs, making it simpler to establish discriminative representations. As for the second strategy, we introduce a new activation function to formulate the Region of Interest (ROI) extraction module that enables the network to focus on relevant regions effectively. It can alleviate the extreme distribution imbalance of samples with different densities. Finally, several experiments show that our method achieves SOTA performance on four challenging datasets.