Architectures of Topological Deep Learning: A Survey on Topological Neural Networks

The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein. Topological Deep Learning (TDL) provides a comprehensive framework to process and extract knowledge from data associated with these systems, such as predicting the social community to which an individual belongs or predicting whether a protein can be a reasonable target for drug development. TDL has demonstrated theoretical and practical advantages that hold the promise of breaking ground in the applied sciences and beyond. However, the rapid growth of the TDL literature has also led to a lack of unification in notation and language across Topological Neural Network (TNN) architectures. This presents a real obstacle for building upon existing works and for deploying TNNs to new real-world problems. To address this issue, we provide an accessible introduction to TDL, and compare the recently published TNNs using a unified mathematical and graphical notation. Through an intuitive and critical review of the emerging field of TDL, we extract valuable insights into current challenges and exciting opportunities for future development

ICML 2023 Topological Deep Learning Challenge:Design and Results

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two month duration. This paper describes the design of the challenge and summarizes its main findings.</p

ICML 2023 topological deep learning challenge. Design and results

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main finding

Successful Thrombectomy Improves Functional Outcome in Tandem Occlusions with a Large Ischemic Core

International audienceBackground: Emergent stenting in tandem occlusions and mechanical thrombectomy (MT) of acute ischemic stroke related to large vessel occlusion (LVO-AIS) with a large core are tested independently. We aim to assess the impact of reperfusion with MT in patients with LVO-AIS with a large core and a tandem occlusion and to compare the safety of reperfusion between large core with tandem and nontandem occlusions in current practice. Methods: We analyzed data of all consecutive patients included in the prospective Endovascular Treatment in Ischemic Stroke Registry in France between January 2015 and March 2023 who presented with a pretreatment ASPECTS (Alberta Stroke Program Early CT Score) of 0–5 and angiographically proven tandem occlusion. The primary end point was a favorable outcome defined by a modified Rankin Scale (mRS) score of 0–3 at 90 days. Results: Among 262 included patients with a tandem occlusion and ASPECTS 0–5, 203 patients (77.5%) had a successful reperfusion (modified Thrombolysis in Cerebral Infarction grade 2b-3). Reperfused patients had a favorable shift in the overall mRS score distribution (adjusted odds ratio [aOR], 1.57 [1.22–2.03]; P < 0.001), higher rates of mRS score 0–3 (aOR, 7.03 [2.60–19.01]; P < 0.001) and mRS score 0–2 at 90 days (aOR, 3.85 [1.39–10.68]; P = 0.009) compared with nonreperfused. There was a trend between the occurrence of successful reperfusion and a decreased rate of symptomatic intracranial hemorrhage (aOR, 0.5 [0.22–1.13]; P = 0.096). Similar safety outcomes were observed after large core reperfusion in tandem and nontandem occlusions. Conclusions: Successful reperfusion was associated with a higher rate of favorable outcome in large core LVO-AIS with a tandem occlusion, with a safety profile similar to nontandem occlusion