Tootfinder

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising
Sergey Mirzoyan
https://arxiv.org/abs/2506.19434 https://

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising
AI-enhanced approaches are becoming common in astronomical data analysis, including in the galaxy morphological classification. In this study we develop an approach that enhances galaxy classification by incorporating an image denoising pre-processing step, utilizing the U-Net Variational Autoencoder (VAE) architecture and effectively mitigating noise in galaxy images and leading to improved classification performance. Our methodology involves training U-Net VAEs on the EFIGI dataset. To simula…

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
Yunsung Chung, Yunbei Zhang, Nassir Marrouche, Jihun Hamm
https://arxiv.org/abs/2506.19360

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
Advances in generative models have transformed the field of synthetic image generation for privacy-preserving data synthesis (PPDS). However, the field lacks a comprehensive survey and comparison of synthetic image generation methods across diverse settings. In particular, when we generate synthetic images for the purpose of training a classifier, there is a pipeline of generation-sampling-classification which takes private training as input and outputs the final classifier of interest. In this…

Exoplanet Classification through Vision Transformers with Temporal Image Analysis
Anupma Choudhary, Sohith Bandari, B. S. Kushvah, C. Swastik
https://arxiv.org/abs/2506.16597

Exoplanet Classification through Vision Transformers with Temporal Image Analysis
The classification of exoplanets has been a longstanding challenge in astronomy, requiring significant computational and observational resources. Traditional methods demand substantial effort, time, and cost, highlighting the need for advanced machine learning techniques to enhance classification efficiency. In this study, we propose a methodology that transforms raw light curve data from NASA's Kepler mission into Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) using the Gramian Angul…

DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
Matt Poyser, Toby P. Breckon
https://arxiv.org/abs/2506.14667

DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
In order to address the scalability challenge within Neural Architecture Search (NAS), we speed up NAS training via dynamic hard example mining within a curriculum learning framework. By utilizing an autoencoder that enforces an image similarity embedding in latent space, we construct an efficient kd-tree structure to order images by furthest neighbour dissimilarity in a low-dimensional embedding. From a given query image from our subsample dataset, we can identify the most dissimilar image wit…

LKA: Large Kernel Adapter for Enhanced Medical Image Classification
Ziquan Zhu, Si-Yuan Lu, Tianjin Huang, Lu Liu, Zhe Liu
https://arxiv.org/abs/2506.19118

LKA: Large Kernel Adapter for Enhanced Medical Image Classification
Despite the notable success of current Parameter-Efficient Fine-Tuning (PEFT) methods across various domains, their effectiveness on medical datasets falls short of expectations. This limitation arises from two key factors: (1) medical images exhibit extensive anatomical variation and low contrast, necessitating a large receptive field to capture critical features, and (2) existing PEFT methods do not explicitly address the enhancement of receptive fields. To overcome these challenges, we propo…

Practical insights on the effect of different encodings, ans\"atze and measurements in quantum and hybrid convolutional neural networks
Jes\'us Lozano-Cruz, Albert Nieto-Morales, Oriol Ball\'o-Gimbernat, Adan Garriga, Ant\'on Rodr\'iguez-Otero, Alejandro Borrallo-Rentero
https://arxiv.org/abs/2506.20355

Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to the task of satellite image classification using the EuroSAT dataset. We systematically evaluate the performance implications of data encoding techniques, variational ansätze, and measurement in approx. 500 distinct model configurations. Our analysis reveals a clear hierarchy of influence on model performance. For h…

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Haolong Jin, Shenglin Liu, Cong Cong, Qingmin Feng, Yongzhi Liu, Lina Huang, Yingzi Hu
https://arxiv.org/abs/2506.15365

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Federated learning (FL) has emerged as a promising approach for collaborative medical image analysis, enabling multiple institutions to build robust predictive models while preserving sensitive patient data. In the context of Whole Slide Image (WSI) classification, FL faces significant challenges, including heterogeneous computational resources across participating medical institutes and privacy concerns. To address these challenges, we propose FedWSIDD, a novel FL paradigm that leverages datas…

J3DAI: A tiny DNN-Based Edge AI Accelerator for 3D-Stacked CMOS Image Sensor
Benoit Tain, Raphael Millet, Romain Lemaire, Michal Szczepanski, Laurent Alacoque, Emmanuel Pluchart, Sylvain Choisnet, Rohit Prasad, Jerome Chossat, Pascal Pierunek, Pascal Vivet, Sebastien Thuries
https://arxiv.org/abs/2506.15316

J3DAI: A tiny DNN-Based Edge AI Accelerator for 3D-Stacked CMOS Image Sensor
This paper presents J3DAI, a tiny deep neural network-based hardware accelerator for a 3-layer 3D-stacked CMOS image sensor featuring an artificial intelligence (AI) chip integrating a Deep Neural Network (DNN)-based accelerator. The DNN accelerator is designed to efficiently perform neural network tasks such as image classification and segmentation. This paper focuses on the digital system of J3DAI, highlighting its Performance-Power-Area (PPA) characteristics and showcasing advanced edge AI c…

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
Sinuo Cheng, Ruyi Zhou, Wenhao Feng, Huaiguang Yang, Haibo Gao, Zongquan Deng, Liang Ding
https://arxiv.org/abs/2506.16703

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
The increasingly complex and diverse planetary exploration environment requires more adaptable and flexible rover navigation strategy. In this study, we propose a VLM-empowered multi-mode system to achieve efficient while safe autonomous navigation for planetary rovers. Vision-Language Model (VLM) is used to parse scene information by image inputs to achieve a human-level understanding of terrain complexity. Based on the complexity classification, the system switches to the most suitable naviga…

Robust Training with Data Augmentation for Medical Imaging Classification
Josu\'e Mart\'inez-Mart\'inez, Olivia Brown, Mostafa Karami, Sheida Nabavi
https://arxiv.org/abs/2506.17133

Robust Training with Data Augmentation for Medical Imaging Classification
Deep neural networks are increasingly being used to detect and diagnose medical conditions using medical imaging. Despite their utility, these models are highly vulnerable to adversarial attacks and distribution shifts, which can affect diagnostic reliability and undermine trust among healthcare professionals. In this study, we propose a robust training algorithm with data augmentation (RTDA) to mitigate these vulnerabilities in medical image classification. We benchmark classifier robustness a…

Normalized Radon Cumulative Distribution Transforms for Invariance and Robustness in Optimal Transport Based Image Classification
Matthias Beckmann, Robert Beinert, Jonas Bresch
https://arxiv.org/abs/2506.08761

Normalized Radon Cumulative Distribution Transforms for Invariance and Robustness in Optimal Transport Based Image Classification
The Radon cumulative distribution transform (R-CDT), is an easy-to-compute feature extractor that facilitates image classification tasks especially in the small data regime. It is closely related to the sliced Wasserstein distance and provably guaranties the linear separability of image classes that emerge from translations or scalings. In many real-world applications, like the recognition of watermarks in filigranology, however, the data is subject to general affine transformations originating…

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Chungang Lin, Yilong Jiang, Weiyao Zhang, Xuying Meng, Tianyu Zuo, Yujun Zhang
https://arxiv.org/abs/2506.14151

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Traffic classification has a significant impact on maintaining the Quality of Service (QoS) of the network. Since traditional methods heavily rely on feature extraction and large scale labeled data, some recent pre-trained models manage to reduce the dependency by utilizing different pre-training tasks to train generic representations for network packets. However, existing pre-trained models typically adopt pre-training tasks developed for image or text data, which are not tailored to traffic d…

I don't recall seeing much measurement research on mobile carrier identification from client traffic. Didn't find much in a cursory search.
Does this exist? Is it feasible, particularly with branded devices like this?
Can classification be done by address pools unique, IDs, MACs, user agent strings, default start-up apps, default DNS queries/settings, etc.?
Clue, ideas, and pointers wanted.

Steve Herman (@w7voa@journa.host)
Attached: 1 image CNBC - A company owned by President Trump announces it own branded Trump Mobile cell phone service and will sell a "T1" smartphone, featuring gold-colored metal case etched with an American flag. https://www.cnbc.com/2025/06/16/trump-mobile-phone-plan.html

Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Ling Li, Yao Zhou, Yuxuan Liang, Fugee Tsung, Jiaheng Wei
https://arxiv.org/abs/2506.14674

Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Previous methods for image geo-localization have typically treated the task as either classification or retrieval, often relying on black-box decisions that lack interpretability. The rise of large vision-language models (LVLMs) has enabled a rethinking of geo-localization as a reasoning-driven task grounded in visual cues. However, two major challenges persist. On the data side, existing reasoning-focused datasets are primarily based on street-view imagery, offering limited scene diversity and…

Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?
Yuesheng Huang, Peng Zhang, Riliang Liu, Jiaqi Liang
https://arxiv.org/abs/2506.17623

Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?
A significant ``modality gap" exists between the abundance of text-only data and the increasing power of multimodal models. This work systematically investigates whether images generated on-the-fly by Text-to-Image (T2I) models can serve as a valuable complementary modality for text-centric tasks. Through a comprehensive evaluation framework on text classification, we analyze the impact of critical variables, including T2I model quality, prompt engineering strategies, and multimodal fusion arch…

Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD Filter-CLAHE
Ach Khozaimi, Isnani Darti, Syaiful Anam, Wuryansari Muharini Kusumawinahyu
https://arxiv.org/abs/2506.15489

Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD Filter-CLAHE
Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on Pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluate…

This https://arxiv.org/abs/2505.24380 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

SASP: Strip-Aware Spatial Perception for Fine-Grained Bird Image Classification
Fine-grained bird image classification (FBIC) is not only of great significance for ecological monitoring and species identification, but also holds broad research value in the fields of image recognition and fine-grained visual modeling. Compared with general image classification tasks, FBIC poses more formidable challenges: 1) the differences in species size and imaging distance result in the varying sizes of birds presented in the images; 2) complex natural habitats often introduce strong ba…

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Amirreza Fateh, Yasin Rezvani, Sara Moayedi, Sadjad Rezvani, Fatemeh Fateh, Mansoor Fateh
https://arxiv.org/abs/2506.14318

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation and classification tasks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by certified radiologists and physicians, spanning three major tumor types-g…

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
Pralay Kumar Das, Nayana Majumdar, Supratik Mukhopadhyay
https://arxiv.org/abs/2506.02506

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
A multi-class convolutional neural network (CNN) model has been developed using Keras deep learning library in Python for image-based classification of $^{12}$C Hoyle state decay branches from tracking information, recorded by Saha Active Target Time Projection Chamber, SAT-TPC (currently under development). The nuclear events, produced by the 30 MeV $α$-particle beam in the SAT-TPC, filled with Ar + CO$_2$ (90:10) gas mixture at atmospheric pressure, have been considered for training and vali…

Congruence conditions for the mod $\lambda$ values of the Fourier coefficients of classical eigenforms
Michael A. Daas
https://arxiv.org/abs/2506.08865 htt…

Congruence conditions for the mod $λ$ values of the Fourier coefficients of classical eigenforms
We classify all instances of the condition $a_{p}(f) \equiv x \bmod λ$ being related to a congruence on the prime $p$, where $a_{p}(f)$ denotes the $p$th Fourier coefficient of a classical normalised cuspidal eigenform $f$ and $λ$ is a prime in the number field generated by the Fourier coefficients of $f$. This classification is done in terms of the (projective) image of the mod $λ$ Galois representation associated with $f$ and extends work by Swinnerton-Dyer. We highlight that for $x = 0$, …

This https://arxiv.org/abs/2505.19111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Remote Sensing Image Classification with Decoupled Knowledge Distillation
To address the challenges posed by the large number of parameters in existing remote sensing image classification models, which hinder deployment on resource-constrained devices, this paper proposes a lightweight classification method based on knowledge distillation. Specifically, G-GhostNet is adopted as the backbone network, leveraging feature reuse to reduce redundant parameters and significantly improve inference efficiency. In addition, a decoupled knowledge distillation strategy is employ…

ViT-based Local Volume dwarf galaxy Identificationin (VIDA) in the CSST survey
Han Qu, Zhen Yuan, Chengliang Wei, Chao Liu, Jiang Chang, Guoliang Li, Nicolas F. Martin, Chaowei Tsai, Shi Shao, Yu Luo, Ran Li, Xi Kang, Xiangxiang Xue, Zhou Fan
https://arxiv.org/abs/2506.04361

ViT-based Local Volume dwarf galaxy Identificationin (VIDA) in the CSST survey
Identifying dwarf galaxies within the Local Volume is crucial for constraining the luminosity function of satellite galaxies in the nearby universe. We report the detection capabilities of dwarf galaxies within the Local Volume using the Chinese Space Station Telescope (CSST). Based on the simulated imaging data of CSST, we develop a detection and classification pipeline that combines traditional image-based search techniques with advanced machine learning classification models. The simulated L…

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Binghao Ye, Wenjuan Li, Dong Wang, Man Yao, Bing Li, Weiming Hu, Dong Liang, Kun Shang
https://arxiv.org/abs/2506.05679

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Spiking Neural Networks (SNNs) are noted for their brain-like computation and energy efficiency, but their performance lags behind Artificial Neural Networks (ANNs) in tasks like image classification and object detection due to the limited representational capacity. To address this, we propose a novel spiking neuron, Integer Binary-Range Alignment Leaky Integrate-and-Fire to exponentially expand the information expression capacity of spiking neurons with only a slight energy increase. This is a…

Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification
Rinat Prochii, Elizaveta Dakhova, Pavel Birulin, Maxim Sharaev
https://arxiv.org/abs/2506.08623

Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification
Accurate classification of second-trimester fetal ultrasound images remains challenging due to low image quality, high intra-class variability, and significant class imbalance. In this work, we introduce a simple yet powerful, biologically inspired deep learning ensemble framework that-unlike prior studies focused on only a handful of anatomical targets-simultaneously distinguishes 16 fetal structures. Drawing on the hierarchical, modular organization of biological vision systems, our model sta…

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
Mana Mohammadi, Amirhesam Jafari Rad, Ashkan Behrouzi
https://arxiv.org/abs/2506.15364

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature extraction. Accurate and timely stroke detection is critical for effective treatment and improved patient outcomes in neuroimaging. While Convolutional Neural Networks (CNNs) are widely used for medical image analysis, their computational complexity often hinders deployment in re…

SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classification
Ali Waqas, Sinem Coleri
https://arxiv.org/abs/2506.10699 https://

SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classification
Sensor-based local inference at IoT devices faces severe computational limitations, often requiring data transmission over noisy wireless channels for server-side processing. To address this, split-network Deep Neural Network (DNN) based Joint Source-Channel Coding (JSCC) schemes are used to extract and transmit relevant features instead of raw data. However, most existing methods rely on fixed network splits and static configurations, lacking adaptability to varying computational budgets and c…

Introduction to Predictive Coding Networks for Machine Learning
Mikko Stenlund
https://arxiv.org/abs/2506.06332 https://arxiv.org/pdf…

Introduction to Predictive Coding Networks for Machine Learning
Predictive coding networks (PCNs) constitute a biologically inspired framework for understanding hierarchical computation in the brain, and offer an alternative to traditional feedforward neural networks in ML. This note serves as a quick, onboarding introduction to PCNs for machine learning practitioners. We cover the foundational network architecture, inference and learning update rules, and algorithmic implementation. A concrete image-classification task (CIFAR-10) is provided as a benchmark…

Recent Advances in Medical Image Classification
Loan Dao, Ngoc Quoc Ly
https://arxiv.org/abs/2506.04129 https://arxiv.org/pdf/2506.04…

Recent Advances in Medical Image Classification
Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solutions: basic, specific, and applied. It highlights advances in traditional methods using deep learning models like Convolutional Neural Networks and Vision Transformers, as well as state-of-the-art approaches with Vision Language Models. These models tackle the issue of limited …

This https://arxiv.org/abs/2505.14049 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification
The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they often suffer from concept leakages, where unintended information within soft concept representations undermines both interpretability and generalizability. Moreover, most concept-based models focus solely on local explanations (instance-level), neglecting the global decision logic (dataset-level). To address these li…

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound
Minglang Chen, Jie He, Caixu Xu, Bocheng Liang, Shengli Li, Guannan He, Xiongjie Tao
https://arxiv.org/abs/2506.06054

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound
ResNet has been widely used in image classification tasks due to its ability to model the residual dependence of constant mappings for linear computation. However, the ResNet method adopts a unidirectional transfer of features and lacks an effective method to correlate contextual information, which is not effective in classifying fetal ultrasound images in the classification task, and fetal ultrasound images have problems such as low contrast, high similarity, and high noise. Therefore, we prop…

Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
Zofia Rudnicka, Januszcz Szczepanski, Agnieszka Pregowska
https://arxiv.org/abs/2506.06265

Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
Spiking Neural Networks (SNNs) event-driven nature enables efficient encoding of spatial and temporal features, making them suitable for dynamic time-dependent data processing. Despite their biological relevance, SNNs have seen limited application in medical image recognition due to difficulties in matching the performance of conventional deep learning models. To address this, we propose a novel breast cancer classification approach that combines SNNs with Lempel-Ziv Complexity (LZC) a computat…

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
Tyler J. Richards, Adam E. Flanders, Errol Colak, Luciano M. Prevedello, Robyn L. Ball, Felipe Kitamura, John Mongan, Maryam Vazirabad, Hui-Ming Lin, Anne Kendell, Thanat Kanthawang, Salita Angkurawaranon, Emre Altinmakas, Hakan Dogan, Paulo Eduardo de Aguiar Kuriki, Arjuna Somasundaram, Christopher Ruston, Deniz Bulja, Naida Spahovic, Jennifer Sommer, Sirui Jiang, Eduardo Moreno Judice de Mattos Farina, Edu…

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar spine examinations annotated for degenerative changes. The dataset includes 2,697 patients with a total of 8,593 image series from 8 institutions across 6 countries and 5 continents. The dataset is available for free for non-commercial use via Kaggle and RSNA Medical Imaging Resource of AI (MIRA). The dataset was cre…

Semi-Automated Quality Assurance in Digital Pathology: Tile Classification Approach
Meredith VandeHaar, M. Clinch, I. Yilmaz, M. A. Rahman, Y. Xiao, F. Dogany, H. M. Alazab, A. Nassar, Z. Akkus, B. Dangott
https://arxiv.org/abs/2506.10916

Semi-Automated Quality Assurance in Digital Pathology: Tile Classification Approach
Quality assurance is a critical but underexplored area in digital pathology, where even minor artifacts can have significant effects. Artifacts have been shown to negatively impact the performance of AI diagnostic models. In current practice, trained staff manually review digitized images prior to release of these slides to pathologists which are then used to render a diagnosis. Conventional image processing approaches, provide a foundation for detecting artifacts on digital pathology slides. H…

Structured Pruning and Quantization for Learned Image Compression
Md Adnan Faisal Hossain, Fengqing Zhu
https://arxiv.org/abs/2506.01229 https://

Structured Pruning and Quantization for Learned Image Compression
The high computational costs associated with large deep learning models significantly hinder their practical deployment. Model pruning has been widely explored in deep learning literature to reduce their computational burden, but its application has been largely limited to computer vision tasks such as image classification and object detection. In this work, we propose a structured pruning method targeted for Learned Image Compression (LIC) models that aims to reduce the computational costs ass…

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu, Nicholas Konz, Yaqian Chen, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2506.12186

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or object segmentation. However, training models for specific new tasks often requires large amounts of labeled data, which is difficult to obtain due to high annotation costs and data privacy concerns. To circumvent this issue, we introduce MRI-CORE (MRI COmprehensive Representat…

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Ziheng Zhao, Lisong Dai, Ya Zhang, Yanfeng Wang, Weidi Xie
https://arxiv.org/abs/2506.03238

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing …

This https://arxiv.org/abs/2305.12844 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Optimized Ensemble Deep Learning Model For Brain Tumor Classification
Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and…