arXiv Machine Learning July 9, 2026 By Cora Jostock, Jonas Ort, Henner Koch, Gregor Schiele, Andreas Erbsl\"oh

Towards a Pseudo-Labeling Workflow for Celltype-Classification from Explanted Brain Slice Recordings

Read the original on arXiv Machine Learning →

arXiv:2607. 06569v1 Announce Type: cross Abstract: This paper proposes an unsupervised workflow to pseudo-label extracellular spikes from human brain slice MEA recordings into two putative cell types: pyramidal cells and interneurons.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

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Unsupervised Learning of Cell Instances with Generative Routing Pyramids

arXiv:2608. 16810v1 Announce Type: cross Abstract: Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis.

By Ziwen Liu, Martin Weigert

computer-vision

arXiv AI

Jul 7

HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy

arXiv:2607. 04353v1 Announce Type: cross Abstract: Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups.

By Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula, Anuun Chinbat, Hannes Leonhard, Moritz Ladenburger, Frederik Alexander, Vishisht Choudhary, Fabio Laredo, Giacomo Masserdotti, Thorben Prein, Carsten Marr, Amirhossein Kardoost

rag diffusion computer-vision efficiency

arXiv AI

Jun 12

Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos

arXiv:2603. 08505v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity.

By Michelle Espranita Liman, \"Ozg\"un Turgut, Alexander M\"uller, Eimo Martens, Daniel Rueckert, Philip M\"uller

multimodal benchmarks

arXiv Machine Learning

Jun 5

Central Description Length (CDL) Clustering Validation Index

arXiv:2606. 05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data.

By Mahdi Shamsi, Soosan Beheshti

rag benchmarks

arXiv AI

Jul 21

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis

arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.

By Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander

computer-vision fine-tuning benchmarks

arXiv AI

Aug 10

Do 3D Medical Foundation Models See Through MRI Artifacts? A Controlled Study of Representation Robustness

arXiv:2608. 06613v1 Announce Type: cross Abstract: Self-supervised 3D medical foundation models are increasingly used as general-purpose feature extractors, yet their sensitivity to MRI artifacts remains poorly understood.

By Julia Anna Mielcarz, Daniel Klaaby, Mostafa Mehdipour Ghazi

computer-vision safety

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