arXiv Machine Learning

MLPTR-CC: Multi-label Pathology Test Recommendation using Classifier Chains and SHAP

arXiv:2607. 08299v2 Announce Type: replace Abstract: Diagnostic decision making often relies on a sequence of pathology tests that bridge patient symptoms and final disease diagnosis.

arXiv Machine Learning

Jul 10

Classifier Chain-based Pathological Test Recommendation

arXiv:2607. 08299v1 Announce Type: new Abstract: Accurate and timely diagnoses are essential for quality patient care.

By Abu Rafe Md Jamil, Nayan Malakar

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Hugging Face Trending Papers

Sep 22

How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification

A key factor in deciding whether to trust an automatic prediction is its confidence score, which should be calibrated to match the actual probability of the prediction being correct. Most confidence c...

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arXiv Computation and Language

Sep 23

How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification

arXiv:2609.26468v1 Announce Type: new Abstract: A key factor in deciding whether to trust an automatic prediction is its confidence score, which should be calibrated to match the actual probability o...

By Sophie Henning, Georg Hofmann, Alexander Schulte, Alexander Fraser, Annemarie Friedrich

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arXiv AI

Jun 8

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.

By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra

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arXiv Machine Learning

Jul 8

Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography

arXiv:2607. 05628v1 Announce Type: cross Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment.

By Mohammad S. Majdi, Jeffrey J. Rodriguez

Hugging Face Trending Papers

Aug 11

RadFusion: Towards Threshold-Controllable Radiology Report Generation

Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content. Such control is essential because clinical scenarios diverge: emergency triage prioritizes sensitivity to reduce missed findings, whereas confirmatory interpretation emphasizes specificity to limit unnecessary interventions.

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arXiv Machine Learning

Jun 5

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.

By Yanqing Luo (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Julius Hense (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Niklas Preni{\ss}l (Institute of Pathology, Charit\'e Universit\"atsmedizin, Berlin, Germany, Berlin Institute of Health at Charit\'e -- Universit\"atsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charit\'e Digital Clinician Scientist Program, Berlin, Germany), Andreas Mock (Institute of Pathology, Ludwig Maximilian University of Munich, Munich, Germany, Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany, NCT Heidelberg, Heidelberg, Germany, German Cancer Consortium), Klaus-Robert M\"uller (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany, Department of Artificial Intelligence, Korea University, Seoul, Korea, Max-Planck Institute for Informatics, Saarbr\"ucken, Germany), Thomas Schnake (Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Canada, Vector Institute for Artificial Intelligence, Toronto, Canada, Acceleration Consortium, University of Toronto, Canada), Mina Jamshidi Idaji (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany)

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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

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arXiv Machine Learning

Jul 23

Plausibility-Driven Prioritization of Candidate Biomedical Annotations

arXiv:2607. 20163v1 Announce Type: cross Abstract: The rapid growth of biomedical knowledge has made the validation of automatically generated biological annotations a major bottleneck in biomedical curation.

By Emanuele Cavalleri, Miad Alavinezhad, Dario Malchiodi, Marco Mesiti

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arXiv AI

Jun 26

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

arXiv:2606. 26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver.

By Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh, Alhatoon Alanazy, Salam Alshammasi, Meshael Almusairii, Shoog Alrassan, Aisha Alansari

Hugging Face Trending Papers

Jun 25

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis.

arXiv Machine Learning

Jul 24

One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification

arXiv:2607. 20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data.

By Afsaneh Mahanipour, Hana Khamfroush

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