# LeJEPA

Paired modalities are usually used to pull two encoders into one shared embedding space. DuoJEPA instead keeps a separate LeJEPA self-supervised space per modality and appends to each encoder one residual-free transformer layer that translates its embedding into the other encoder's space, trained by a contrastive loss computed entirely within that target space. On CheXpert Plus chest radiographs and reports it reaches a zero-shot AUC of 86.6 and a cross-modal retrieval score of 78.5, against 60.0 and 64.2 for a reimplementation of LeVLJEPA with the same encoders and data, while a paired-cosine test detects no direct alignment between the two own spaces.

Paired modalities are usually used to pull two encoders into one shared embedding space. DuoJEPA instead keeps a separate LeJEPA self-supervised space per modality and appends to each encoder one residual-free transformer layer that translates its embedding into the other encoder's space, trained by a contrastive loss computed entirely within that target space. On CheXpert Plus chest radiographs and reports it reaches a zero-shot AUC of 86.6 and a cross-modal retrieval score of 78.5, against 60.0 and 64.2 for a reimplementation of LeVLJEPA with the same encoders and data, while a paired-cosine test detects no direct alignment between the two own spaces.

Radiolucent foreign body aspiration in adults sits at soft-tissue and mucus density on CT, so it has been found only by slice-by-slice manual reading and is easily missed. AI-PACS instead learns a standard Gaussian manifold of non-FBA airway topology from 804 unlabelled CT scans, flags deviations from that manifold as anomalies, and maps them onto the S1–S10 bronchial segments. In a 35-patient human-AI subset, sensitivity rose from 18.8% for radiologists to 56.3%, and average negative-exclusion screening time fell by about 75%.

Radiolucent foreign body aspiration in adults sits at soft-tissue and mucus density on CT, so it has been found only by slice-by-slice manual reading and is easily missed. AI-PACS instead learns a standard Gaussian manifold of non-FBA airway topology from 804 unlabelled CT scans, flags deviations from that manifold as anomalies, and maps them onto the S1–S10 bronchial segments. In a 35-patient human-AI subset, sensitivity rose from 18.8% for radiologists to 56.3%, and average negative-exclusion screening time fell by about 75%.