cs papers | Gist.Science

DeepSeek for Pathology Report Understanding: A Benchmark Study of Cancer Type Extraction, AJCC Staging, and Prognosis Prediction

This benchmark study demonstrates that while DeepSeek models effectively extract cancer types and predict AJCC stages from 952 unstructured pathology reports, DeepSeek-extracted structured variables did not significantly improve prognosis prediction under the evaluated framework compared with using the original pathology report text directly, supporting a hybrid architecture that leverages DeepSeek for information extraction and staging while relying on traditional machine learning for outcome modeling.

Mohamed Kone, Gaoussou Haidara, Chao Hou, Shulin Wang2026-07-17✓ Author reviewed ⓘ

💻 computer science

Conv-Guided Mamba Vision Transformer and SPP-DenseNet121 based Hybrid Multi-Feature Fusion and Query Expansion in Content-Based Medical Image Retrieval

This paper proposes a novel Content-Based Medical Image Retrieval framework that integrates a Conv-Guided Mamba Vision Transformer with SPP-DenseNet121 for hybrid multi-feature fusion and employs query expansion to achieve high retrieval accuracy on endoscopic bladder tissue and gastrointestinal datasets.

Mathana Gopal Arulsamy, Pallikonda Rajasekaran Murugan, Md. Jakir Hossen, Wai Kit Wong, Poh Kiat Ng, Gomathy Nayagam M2026-07-17

💻 computer science

A Spectrogram-Based Deep Learning Framework for Automatic Swara and Gamaka Recognition in Carnatic Veena Performances

This paper proposes a spectrogram-based deep learning framework utilizing Short-Time Fourier Transform and Mel spectrograms with convolutional neural networks to automatically recognize swaras and gamakas in Carnatic Veena performances, addressing unique acoustic challenges and establishing a foundation for applications in transcription, education, and digital archiving.

SUDHI S2026-07-17

💻 computer science

Human-in-the-loop GenAI advising for community college pathway decision-making: a design-based case study of requirement-group modeling and privacy-preserving student-support design

This design-based case study addresses the gap in GenAI research regarding community college advising by proposing a privacy-preserving framework and eight design principles that position AI as an explanatory layer for pathway planning while maintaining human oversight, protecting student data, and fostering learner agency.

Emery Peck2026-07-17

💻 computer science

Zero-shot Generalizable LiDAR Semantic Segmentation via Scene-Sensor Disentanglement

This paper introduces LiSeer, a zero-shot generalizable LiDAR semantic segmentation framework that achieves robust cross-sensor performance by disentangling scene-intrinsic geometry from sensor-specific sampling effects through a diffusion-based scene reconstruction and controllable sampling pipeline.

Shaobing Xu, Zikun Xu, Shuocheng Yang, Jiahao Wang, Keqiang Li, Jianqiang Wang2026-07-17

💻 computer science

Goal-Aware Adaptive Regulation Across Modalities: Evolutionary Architecture Search for Transformer Language Models on a Consumer AMD GPU

This paper demonstrates that the GaaR evolutionary architecture search framework, originally validated on convolutional networks, successfully transfers to autoregressive transformer language models on a consumer AMD GPU via DirectML, achieving competitive performance and revealing that regulated exploitation outperforms random search on structured landscapes while favoring wide-shallow architectures under fixed step budgets.

Leon Sandler2026-07-17

💻 computer science

A Distributed CatBoost Approach with Weighted Aggregation for STI/HIV Risk Prediction

This paper proposes a privacy-preserving, distributed CatBoost framework with weighted aggregation that achieves high accuracy in predicting STI and HIV risks across eight countries without requiring centralized data, thereby offering a robust alternative to traditional centralized machine learning models.

Zhaohui Tang, Yan Li, Abdulkadir Sengur, U. Rajendra Acharya2026-07-17

💻 computer science

NAE-VC: Neural Arithmetic Encoding as a Learned Replacement for CABAC in Modern Video Codecs

NAE-VC is a modular, learned entropy coding framework that replaces the standard CABAC engine in H.264, H.265, and H.266 codecs with a neural architecture combining context aggregation, Gaussian mixture modeling, and hyperpriors to achieve significant bitrate savings while maintaining full compatibility with existing video coding standards.

Reka Sandaruwan Gallena Watthage, Anil Fernando2026-07-17

💻 computer science

MUNNA: A Multi-strategy, Uncertainty-driven, Nested Neuro-ensemble Algorithm for Disease Diagnosis with Closed-loop Explainability Feedback

This paper introduces MUNNA, a novel three-stage hybrid framework that integrates a GWO-SA metaheuristic for feature selection, an uncertainty-weighted stacking ensemble, and a closed-loop SHAP feedback mechanism to achieve state-of-the-art disease diagnosis performance on the Breast Cancer Wisconsin dataset while addressing limitations in local optima stagnation and static ensemble weighting.

Md. Maruf Sharker Munna2026-07-17

💻 computer science

DAÏO: A High-Specificity Real-TimeVision-Based Fall Detection SystemUsing Advanced Kinematic Tracking andComputer Vision

The paper presents DAÏO, a novel edge-capable vision-based fall detection system that combines a lightweight pose estimator, an adaptive Extended Kalman Filter, and a Triple-Check Logic Engine to achieve high specificity (96.4%) and significantly reduce false positives from daily activities and religious prostrations while maintaining real-time performance on consumer hardware.

Alamin Abubakar Nataala2026-07-17

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