Hugging Face Trending Papers

ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets

Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly while relying on irrelevant cues, shortcut associations, peripheral structures, or device level artifacts instead of task relevant regions.

arXiv AI

Jul 9

ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets

arXiv:2607. 06889v1 Announce Type: cross Abstract: Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed.

By Abhay Kumar Pathak, Mrityunjay Chaubey, Manjari Gupta

computer-vision

arXiv Machine Learning

Jul 30

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.

By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger

llms fine-tuning multimodal safety

Hugging Face Trending Papers

Jun 29

SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation

Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image.

llms multimodal benchmarks

arXiv Machine Learning

Jul 24

Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification

arXiv:2607. 21068v1 Announce Type: new Abstract: Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized.

By Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya Chatterjee

rag benchmarks safety

arXiv AI

Oct 7

MedCORE: Criteria-Grounded Clinical Reasoning for Interpretable Medical Image Diagnosis

arXiv:2610.08528v1 Announce Type: cross Abstract: Clinical diagnosis is inherently a structured reasoning process, yet existing deep learning models often bypass this structure by mapping image featu...

By Asim Khan, Samee Ullah Khan, Dwarikanath Mahapatra

llms multimodal

arXiv AI

Aug 13

Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations

arXiv:2608. 12299v1 Announce Type: cross Abstract: Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence.

By AmirHossein Eshghi, Hamid Saadatfar, Seyyed Ali Hoseini, AmirMohsen Eshghi, Siavash Arjomand Bigdel

llms computer-vision benchmarks

arXiv Computer Vision

Sep 22

Transferring Visual Explanations: How Cross-Architecture Knowledge Distillation Affects Model Interpretability

arXiv:2609.23561v1 Announce Type: new Abstract: Deploying efficient neural networks is essential in resource-constrained environments, yet compact models often sacrifice interpretability - a critical...

By Aleks Czufarow, Ihor Babin

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arXiv AI

Sep 15

GP-VM$\times$SMA: Benchmarking General-Purpose Vision Models and Specialized Architectures for 2D Medical Image Segmentation

arXiv:2603.13044v2 Announce Type: replace-cross Abstract: Medical image segmentation (MIS) is a fundamental component of computer-assisted diagnosis and clinical decision support. Over the past decad...

By Vanessa Borst, Anna Riedmann, Samuel Kounev

computer-vision benchmarks

arXiv AI

Jul 17

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening

arXiv:2607. 15047v1 Announce Type: cross Abstract: Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries.

By Javad Khoramdel, Farhad Hoseyni, Amirhossein Nikoofard

rag fine-tuning

arXiv Machine Learning

Aug 13

Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIP

arXiv:2608. 12086v1 Announce Type: cross Abstract: Vision-language models, such as contrastive language-image pre-training (CLIP)-based approaches, have reached state-of-the-art (SOTA) results in medical artificial intelligence.

By Nikolette Pedersen, Regitze Sydendal, Veronika Cheplygina, Th\'eo Sourget

llms multimodal benchmarks data-engineering

arXiv AI

Jul 7

PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification

arXiv:2607. 04478v1 Announce Type: cross Abstract: Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures.

By Moshiur Rahman, Shafqat Alam, Tasnia Binte Mamun

benchmarks

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

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