arXiv AI August 18, 2026 By Hadi Hasan, Safaa Salman, Lama Sleem, Ralph Mouawad, Ali Chehab

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

arXiv:2606. 07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction.

By Spoorthi M, Suja Palaniswamy

llms computer-vision benchmarks

Hugging Face Trending Papers

Jun 21

Multi-cancer detection using a computationally efficient CNN with transfer learning

This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detection using biomedical images. The proposed lightweight CNN model is designed to reduce computational complexity while maintaining high classification performance, making it suitable for deployment in resource-constrained environments.

fine-tuning benchmarks

arXiv Machine Learning

Jul 30

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.

By Nand Lal Yadav, Rajesh Kumar, Satyendra Singh, Sudhakar Singh

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 24

Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning

arXiv:2508. 17728v2 Announce Type: replace-cross Abstract: Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women.

By Nisreen Albzour, Sarah S. Lam

computer-vision

arXiv AI

Jul 7

Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images

arXiv:2607. 03253v1 Announce Type: cross Abstract: As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular clinical relevance.

By Ivica Kopriva, Dario Sitnik, Arijana Pacic, Karolina Krstanac, Irena Veliki Dalic, Marijana Popovic Hadzija

computer-vision

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AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

Multi-cancer detection using a computationally efficient CNN with transfer learning

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

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

Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning

Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images