eess.IV · 2026-08-03 · No. 73

Image and Video Processing, 2026-08-03.

2 new papers in eess.IV. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

    Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo, Shyam Nandan Rai, Christian Ledig

    eess.IV · cs.CV · cs.LG

    Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates. Although PEFT successfully navigates limited data scenarios, it inherently forces the training of a separate, isolated adapter for every specific diagnostic task. Consolidating these isolated adapters into a single generalist network risks...

    arxiv.org/abs/2607.29462 · PDF

  2. 02

    Few-shot Deep Learning for Phase-Amplitude Aberration Correction in Transcranial Focused Ultrasound

    Minju Seol, Minjee Seo, Seonaeng Cho, Kyungho Yoon

    eess.IV · cs.LG

    Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and therapeutic applications. However, the heterogeneous structure of the skull induces complex, patient-specific phase and amplitude aberrations that distort the acoustic focus and deviate it from the intended target, compromising therapeutic efficacy and safety. Conventional time-reversal (TR)...

    arxiv.org/abs/2607.29182 · PDF

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