{"list":"other","total":73,"rows":[{"id":"2402.03300","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","created":"2024-02-05","cited_by_count":9379},{"id":"2502.13923","title":"Qwen2.5-VL Technical Report","created":"2025-02-19","cited_by_count":5587},{"id":"2408.00714","title":"SAM 2: Segment Anything in Images and Videos","created":"2024-08-01","cited_by_count":2863},{"id":"2503.14476","title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","created":"2025-03-18","cited_by_count":2810},{"id":"2409.19256","title":"HybridFlow: A Flexible and Efficient RLHF Framework","created":"2024-09-28","cited_by_count":2057},{"id":"2102.04306","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","created":"2021-02-08","cited_by_count":1558},{"id":"1807.10165","title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","created":"2018-07-18","cited_by_count":1525},{"id":"2403.13372","title":"LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models","created":"2024-03-20","cited_by_count":1464},{"id":"2312.07104","title":"SGLang: Efficient Execution of Structured Language Model Programs","created":"2023-12-12","cited_by_count":1141},{"id":"2504.07615","title":"VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model","created":"2025-04-10","cited_by_count":526},{"id":"2503.01785","title":"Visual-RFT: Visual Reinforcement Fine-Tuning","created":"2025-03-03","cited_by_count":513},{"id":"2304.12620","title":"Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation","created":"2023-04-25","cited_by_count":490},{"id":"2503.21776","title":"Video-R1: Reinforcing Video Reasoning in MLLMs","created":"2025-03-27","cited_by_count":484},{"id":"2003.10286","title":"PathVQA: 30000+ Questions for Medical Visual Question Answering","created":"2020-03-07","cited_by_count":475},{"id":"2006.11392","title":"PraNet: Parallel Reverse Attention Network for Polyp Segmentation","created":"2020-06-13","cited_by_count":460},{"id":"2311.03356","title":"GLaMM: Pixel Grounding Large Multimodal Model","created":"2023-11-06","cited_by_count":455},{"id":"2102.09542","title":"SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering","created":"2021-02-18","cited_by_count":449},{"id":"2507.05201","title":"MedGemma Technical Report","created":"2025-07-07","cited_by_count":436},{"id":"2505.14362","title":"DeepEyes: Incentivizing \"Thinking with Images\" via Reinforcement Learning","created":"2025-05-20","cited_by_count":410},{"id":"2206.08023","title":"AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation","created":"2022-06-16","cited_by_count":351},{"id":"2505.15966","title":"Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning","created":"2025-05-21","cited_by_count":278},{"id":"2503.07536","title":"LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL","created":"2025-03-10","cited_by_count":248},{"id":"2503.06520","title":"Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement","created":"2025-03-09","cited_by_count":239},{"id":"2506.07044","title":"Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning","created":"2025-06-08","cited_by_count":230},{"id":"1910.03667","title":"REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs","created":"2019-10-08","cited_by_count":228},{"id":"2003.13145","title":"Can AI help in screening Viral and COVID-19 pneumonia?","created":"2020-03-29","cited_by_count":200},{"id":"2402.09181","title":"OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM","created":"2024-02-14","cited_by_count":184},{"id":"2406.02918","title":"U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation","created":"2024-06-05","cited_by_count":179},{"id":"2502.19634","title":"MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning","created":"2025-02-26","cited_by_count":173},{"id":"2505.08617","title":"OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning","created":"2025-05-13","cited_by_count":163},{"id":"2503.13939","title":"Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models","created":"2025-03-18","cited_by_count":144},{"id":"2509.07969","title":"Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search","created":"2025-09-09","cited_by_count":120},{"id":"2405.12971","title":"BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once","created":"2024-05-21","cited_by_count":111},{"id":"2307.01984","title":"The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT","created":"2023-07-05","cited_by_count":103},{"id":"2411.16044","title":"ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration","created":"2024-11-25","cited_by_count":103},{"id":"2510.08668","title":"Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding","created":"2025-10-09","cited_by_count":102},{"id":"2408.03361","title":"GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI","created":"2024-08-06","cited_by_count":96},{"id":"2504.03600","title":"MedSAM2: Segment Anything in 3D Medical Images and Videos","created":"2025-04-04","cited_by_count":96},{"id":"2304.06131","title":"UniverSeg: Universal Medical Image Segmentation","created":"2023-04-12","cited_by_count":91},{"id":"2508.04416","title":"Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning","created":"2025-08-06","cited_by_count":91},{"id":"2505.19255","title":"VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use","created":"2025-05-25","cited_by_count":90},{"id":"2010.11364","title":"Sample Efficient Reinforcement Learning with REINFORCE","created":"2020-10-22","cited_by_count":86},{"id":"1906.03720","title":"Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm","created":"2019-06-09","cited_by_count":77},{"id":"2306.13465","title":"3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation","created":"2023-06-23","cited_by_count":70},{"id":"2106.04463","title":"A multi-centre polyp detection and segmentation dataset for generalisability assessment","created":"2021-06-08","cited_by_count":63},{"id":"2107.05023","title":"NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection","created":"2021-07-11","cited_by_count":51},{"id":"2403.08002","title":"Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation","created":"2024-03-12","cited_by_count":51},{"id":"2411.12915","title":"VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge","created":"2024-11-19","cited_by_count":50},{"id":"2304.03493","title":"UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner","created":"2023-04-07","cited_by_count":47},{"id":"2506.22624","title":"Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning","created":"2025-06-27","cited_by_count":44},{"id":"2502.19655","title":"Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning","created":"2025-02-27","cited_by_count":40},{"id":"2411.17606","title":"HyperSeg: Towards Universal Visual Segmentation with Large Language Model","created":"2024-11-26","cited_by_count":33},{"id":"2407.02235","title":"Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation","created":"2024-07-02","cited_by_count":31},{"id":"2505.22596","title":"SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning","created":"2025-05-28","cited_by_count":31},{"id":"2411.12814","title":"Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline","created":"2024-11-19","cited_by_count":30},{"id":"2503.08625","title":"SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories","created":"2025-03-11","cited_by_count":30},{"id":"2412.09278","title":"Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine","created":"2024-12-12","cited_by_count":28},{"id":"2312.08078","title":"Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation","created":"2023-12-13","cited_by_count":26},{"id":"2410.18387","title":"Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks","created":"2024-10-24","cited_by_count":24},{"id":"2505.20202","title":"PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology","created":"2025-05-26","cited_by_count":24},{"id":"2507.02994","title":"MedGround-R1: Advancing Medical Image Grounding via Spatial-Semantic Rewarded Group Relative Policy Optimization","created":"2025-07-01","cited_by_count":23},{"id":"2311.17081","title":"I-MedSAM: Implicit Medical Image Segmentation with Segment Anything","created":"2023-11-28","cited_by_count":22},{"id":"2505.23601","title":"EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis","created":"2025-05-29","cited_by_count":14},{"id":"2510.10052","title":"Think Twice to See More: Iterative Visual Reasoning in Medical VLMs","created":"2025-10-11","cited_by_count":13},{"id":"2503.04639","title":"Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation","created":"2025-03-06","cited_by_count":12},{"id":"2510.15710","title":"UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis","created":"2025-10-17","cited_by_count":12},{"id":"2504.21336","title":"UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation","created":"2025-04-30","cited_by_count":10},{"id":"2508.00669","title":"Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications","created":"2025-08-01","cited_by_count":10},{"id":"2508.08177","title":"MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision","created":"2025-08-11","cited_by_count":10},{"id":"2512.14157","title":"Ophiuchus: Incentivizing Tool-augmented \"Think with Images\" for Joint Medical Segmentation, Understanding and Reasoning","created":"2025-12-16","cited_by_count":10},{"id":"2601.03054","title":"IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation","created":"2026-01-06","cited_by_count":10},{"id":"2509.19090","title":"Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning","created":"2025-09-23","cited_by_count":8},{"id":"2601.01868","title":"DermoGPT: Open Weights and Open Data for Morphology-Grounded Dermatological Reasoning MLLMs","created":"2026-01-05","cited_by_count":3}],"next_cursor":null}