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#few-shot-learning

4 curated events
papersTODAY 04:00 UTC

ProtoCAM: Few-Shot Prototypical Learning for Breast Lesion Classification in Ultrasound

Researchers propose ProtoCAM, a method that combines prototypical networks with mask guidance to classify breast lesions in ultrasound images using only a small number of labeled examples. The approach is designed to be interpretable, addressing the difficulty of building reliable deep learning models for ultrasound analysis, especially in patients with dense breast tissue. The work is published as an arXiv preprint.

medical-imaging/few-shot-learning/interpretability/arxiv
papersTODAY 04:00 UTC

Synthetic Data Method Targets Few-Shot Cryo-ET Subtomogram Classification

A new arXiv paper addresses the limited availability of labeled data for subtomogram classification in cryo-electron tomography. The authors propose a method to close the gap between simulated cryo-ET data and real experimental images, aiming to improve classification when only a few labeled examples exist. The approach is categorized under machine learning research.

cryo-et/few-shot-learning/synthetic-data/research
papersSEP 10 04:00 UTC

Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification

A newly updated arXiv paper introduces a classification approach that combines spatial and frequency-domain representations, with particular emphasis on phase information, to better capture structural relationships between visually similar images. The method targets few-shot fine-grained classification, where a model must distinguish closely related categories using only a small number of labeled examples.

few-shot-learning/computer-vision/image-classification
papersSEP 10 04:00 UTC

ProMeta: few-shot learning predicts PROTAC degradation across E3 ligases

Researchers introduce ProMeta, a few-shot machine learning framework that forecasts how effectively PROTAC molecules degrade target proteins across different E3 ligases. PROTACs are bifunctional compounds that hijack the ubiquitin-proteasome system to eliminate disease-linked proteins long considered out of reach for conventional drugs. The method targets the scarcity of labeled data that has limited prior computational predictors for targeted degradation.

machine-learning/drug-discovery/few-shot-learning/biotech