arXiv AI

A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation

arXiv:2608. 02805v1 Announce Type: cross Abstract: In previous work, we integrated large language models (LLMs) into the lesion segmentation model based on the ULS23 DeepLesion dataset, using short-form findings from the reports.

arXiv AI

22h ago

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses

arXiv:2311. 09614v5 Announce Type: replace-cross Abstract: This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature.

By Shadab Ahamed, Yixi Xu, Sara Kurkowska, Claire Gowdy, Joo H. O, Ingrid Bloise, Don Wilson, Patrick Martineau, Fran\c{c}ois B\'enard, Fereshteh Yousefirizi, Rahul Dodhia, Juan M. Lavista, William B. Weeks, Carlos F. Uribe, Arman Rahmim

computer-vision

arXiv AI

1d ago

Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology

arXiv:2608. 15915v1 Announce Type: cross Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination.

By Hadi Hasan, Safaa Salman, Lama Sleem, Ralph Mouawad, Ali Chehab

llms computer-vision benchmarks

arXiv Machine Learning

Jul 27

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.

By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska

computer-vision benchmarks

arXiv Machine Learning

Jun 19

Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

arXiv:2606. 20477v1 Announce Type: cross Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations.

By Yusuf Salcan (Computer Vision Group, University of Freiburg, Germany, CRIION-AI Lab, Freiburg, Germany), Simon Ging (Computer Vision Group, University of Freiburg, Germany, Adaptive & Agentic AI), Robin Schirrmeister (Department of Radiology, Medical Center -- University of Freiburg, Germany), Philipp Arnold (Department of Radiology, Medical Center -- University of Freiburg, Germany), Elmar Kotter (Department of Radiology, Medical Center -- University of Freiburg, Germany), Behzad Bozorgtabar (Adaptive & Agentic AI), Thomas Brox (Computer Vision Group, University of Freiburg, Germany)

llms computer-vision nlp fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 4

RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI

arXiv:2608. 00508v1 Announce Type: cross Abstract: Object detection and segmentation in three-dimensional medical images is a very active area of research.

By Kai Geissler, Laurens M\"uller-Groh, Hans Meine

computer-vision

arXiv AI

Jun 10

++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation

arXiv:2606. 10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data.

By Ana Sofia Santos, Andr\'e Ferreira, Gijs Luijten, Naida Solak, Lisle Faray de Paiva, Behrus Hinrichs-Puladi, Jens Kleesiek, Jan Egger, Victor Alves

computer-vision safety

arXiv Machine Learning

Jul 10

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.

By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli

llms multimodal benchmarks

arXiv Machine Learning

1d ago

Comprehensive language-image pre-training for 3D medical image understanding

arXiv:2510. 15042v3 Announce Type: replace-cross Abstract: In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports.

By Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao, Sam Bond-Taylor, Harshita Sharma, Maximilian Ilse, Cynthia Lo, Olesya Melnichenko, Anton Schwaighofer, Noel C. F. Codella, Maria Teodora Wetscherek, Klaus H. Maier-Hein, Panagiotis Korfiatis, Valentina Salvatelli, Javier Alvarez-Valle, Fernando P\'erez-Garc\'ia

computer-vision multimodal benchmarks safety

arXiv AI

Jun 30

RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning

arXiv:2606. 28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal.

By Jiasheng Wang, Tanun Jitwatcharakomol, Piyawadee Jongpradubgiat, Simeng Zhu

llms computer-vision reinforcement-learning

arXiv Machine Learning

Jul 30

BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens

arXiv:2607. 26829v1 Announce Type: cross Abstract: Many high-performing volumetric segmentation models maintain dense multi-scale feature maps, leading to high activation memory and inference cost.

By David Hagerman, Roman Naeem, Fredrik Kahl

computer-vision

arXiv Machine Learning

Jul 22

Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation

arXiv:2512. 08216v4 Announce Type: replace-cross Abstract: Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment.

By Aneesh Rangnekar, Harini Veeraraghavan

llms computer-vision benchmarks safety

arXiv AI

Jun 4

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

arXiv:2606. 04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level.

By Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun, Jiang Bian, Russell Terry, Jie Xu

computer-vision

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