mlsanityData to 14 Oct 2026

A Deep Learning Approach for Augmenting Perceptional Understanding of Histopathology Images

arXiv 2503.06894 ↗First posted 10 Mar 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

In Recent Years, Digital Technologies Have Made Significant Strides In Augmenting-Human-Health, Cognition, And Perception, Particularly Within The Field Of Computational-Pathology. This Paper Presents A Novel Approach To Enhancing The Analysis Of Histopathology Images By Leveraging A Mult-modal-Model That Combines Vision Transformers (Vit) With Gpt-2 For Image Captioning. The Model Is Fine-Tuned On The Specialized Arch-Dataset, Which Includes Dense Image Captions Derived From Clinical And Academic Resources, To Capture The Complexities Of Pathology Images Such As Tissue Morphologies, Staining Variations, And Pathological Conditions. By Generating Accurate, Contextually Captions, The Model Augments The Cognitive Capabilities Of Healthcare Professionals, Enabling More Efficient Disease Classification, Segmentation, And Detection. The Model Enhances The Perception Of Subtle Pathological Features In Images That Might Otherwise Go Unnoticed, Thereby Improving Diagnostic Accuracy. Our Approach Demonstrates The Potential For Digital Technologies To Augment Human Cognitive Abilities In Medical Image Analysis, Providing Steps Toward More Personalized And Accurate Healthcare Outcomes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 10 Mar 2025.

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References · 15

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3 more
  1. 2021Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Networkcited by 3
  2. 2022Universal Evasion Attacks on Summarization Scoringcited by 2
  3. 2024Introducing IHARDS-CNN: A Cutting-Edge Deep Learning Method for Human Activity Recognition Using Wearable Sensorscited by 1

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