arXiv Machine Learning August 3, 2026 By Priya Tomar, Aditya Parikh, Christian Bauckhage, Rafet Sifa

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation

Read the original on arXiv Machine Learning →

arXiv:2607. 29509v1 Announce Type: cross Abstract: Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance, which results from relatively lower proportions of small and limitedly exposed structures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Computer Vision

Aug 26

Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation

arXiv:2608.24541v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding...

By Xinning Yao, Jingjing Wang, Jinghua Yue, Xiaoyan Luo, Fugen Zhou, Bo Liu

computer-vision benchmarks safety

Hugging Face Trending Papers

Aug 25

Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation

Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding and clinical assistance. Recently, adapting foun...

computer-vision benchmarks safety

arXiv Computer Vision

Aug 26

B-MIM: Biased Masked Image Modeling for Generalizable Segmentation of Fine-Grained Anatomical Structures

arXiv:2608.24364v1 Announce Type: new Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic underst...

By Sebasti\'an Gonz\'alez, Karen Sanchez, Jos\'e M. Saavedra, Marcelo Pizarro, Bernard Ghanem

llms computer-vision fine-tuning safety

arXiv AI

Jul 29

OrganLens: Organ-Specific Representation Learning for CT Foundation Models

arXiv:2607. 25164v1 Announce Type: cross Abstract: A CT examination captures multiple organs, but many biomedical questions concern abnormalities, prognosis, or longitudinal change in a specific organ.

By Zhixuan Ge, Anqi Li, Sadeer Al-Kindi, Hanwen Xu, Wei Qiu

diffusion computer-vision efficiency

arXiv AI

Sep 18

Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation

The paper proposes a two‑stage learning framework for multi‑organ segmentation that handles partially annotated datasets and domain shifts. First, the model learns accurate segmentations from available annotations to build robust feature representations. Second, it introduces learnable organ prototypes and a Sinkhorn‑triplet loss to enforce organ‑wise feature consistency across datasets, keeping embeddings of the same organ close while separating different organs, even when annotations are missing.

By Dakini Mallam Garba, Salim Abdou Daoura

rag computer-vision benchmarks safety

arXiv AI

Jun 16

K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model

arXiv:2509. 25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented.

By Bangwei Guo, Yunhe Gao, Meng Ye, Difei Gu, Yang Zhou, Leon Axel, Dimitris Metaxas

computer-vision benchmarks

One email a morning, machine-written

Related stories

Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation

Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation

B-MIM: Biased Masked Image Modeling for Generalizable Segmentation of Fine-Grained Anatomical Structures

OrganLens: Organ-Specific Representation Learning for CT Foundation Models

Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation

K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model