MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images | Paper Citation Record

Observation 3c3ce95f-2fff-442d-bf03-29e7e4f5d134 · outbound

This paper cites GPT-4 Technical Report.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images GPT-4 Technical Report

Reference 1

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Observation 92280afd-1845-486d-b210-230ac4705620 · outbound

This paper cites The medical segmentation decathlon.Nature communications, 13(1):4128, 2022.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images The medical segmentation decathlon.Nature communications, 13(1):4128, 2022

Reference 2

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Observation 87494567-1b58-489b-89ee-dad8ba3498f7 · outbound

This paper cites Qwen Technical Report.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Qwen Technical Report

Reference 3

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Observation 954e02a2-f4c2-4244-9966-b9bfb629fca7 · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 4

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Observation d5842745-9efd-4bb9-a1c8-0711a4120a10 · outbound

This paper cites SAM-Med2D.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images SAM-Med2D

Reference 5

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Observation ceff7725-ced6-4358-8e42-f3b7b36bbea4 · outbound

This paper cites Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline

Reference 6

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Observation d118079c-f8b4-468b-a2c8-23e152aae5da · outbound

This paper cites The importance of skip connections in biomedical image segmentation.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images The importance of skip connections in biomedical image segmentation

Reference 7

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Observation 87d89f19-671d-46c3-9d61-cf099433e6a9 · outbound

This paper cites Segvol: Universal and interactive volumetric medical image segmen- tation.Advances in Neural Information Processing Systems, 37:110746–110783, 2024.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Segvol: Universal and interactive volumetric medical image segmen- tation.Advances in Neural Information Processing Systems, 37:110746–110783, 2024

Reference 8

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Observation ced231b1-d5dc-4c8f-8969-06e172cbe3f0 · outbound

This paper cites The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 9

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Observation 671d2631-4475-450f-a3d7-aa87126f0c94 · outbound

This paper cites Intracranial hemorrhage segmentation using a deep convolutional model.Data, 5(1):14, 2020.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Intracranial hemorrhage segmentation using a deep convolutional model.Data, 5(1):14, 2020

Reference 10

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Observation 07ec0484-6e8a-4457-91b7-7c3dc2274da3 · outbound

This paper cites Cross-modal conditioned recon- struction for language-guided medical image segmentation.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Cross-modal conditioned recon- struction for language-guided medical image segmentation

Reference 11

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Observation 37b9aaf9-bb57-4030-a9f1-6b8988b4bfd9 · outbound

This paper cites Towards a multimodal large language model with pixel-level insight for biomedicine.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Towards a multimodal large language model with pixel-level insight for biomedicine

Reference 12

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Observation 2accde91-84fa-4114-bc30-34eefb858976 · outbound

This paper cites MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models

Reference 13

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Observation 083535c2-6034-47d6-8649-54abbd24e769 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211, 2021.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211, 2021

Reference 14

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Observation 3911d69f-4b7e-483d-806d-522cbe082145 · outbound

This paper cites Harnessing progress in radiotherapy for global cancer control.Nature Cancer, 4(9):1228–1238,.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Harnessing progress in radiotherapy for global cancer control.Nature Cancer, 4(9):1228–1238,

Reference 15

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Observation 451c6385-72f3-4e0b-86b0-fdc3ab35a67e · outbound

This paper cites MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation

Reference 16

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Observation 2dccb4d4-aaaf-4056-9718-ca27bb7f319e · outbound

This paper cites Segment any- thing.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Segment any- thing

Reference 17

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Observation 8830a522-853e-4652-8a7f-0ee8f454932a · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Lisa: Reasoning segmentation via large language model

Reference 18

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Observation cc7e6179-f86c-44f3-9d0e-eebbcb1bef4c · outbound

This paper cites Llava-med: Training a large language- and-vision assistant for biomedicine in one day.Advances in Neural Information Processing Systems, 36:28541–28564,.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Llava-med: Training a large language- and-vision assistant for biomedicine in one day.Advances in Neural Information Processing Systems, 36:28541–28564,

Reference 19

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Observation a1a37bbf-a8e7-468a-bbc7-44cdc7da1232 · outbound

This paper cites Lvit: language meets vision transformer in medical image seg- mentation.IEEE transactions on medical imaging, 43(1): 96–107, 2023.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Lvit: language meets vision transformer in medical image seg- mentation.IEEE transactions on medical imaging, 43(1): 96–107, 2023

Reference 20

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Observation bee10bb0-cd96-4fdf-9dce-f612f281f844 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

Reference 21

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Observation d403f3f7-f38b-44e2-b57e-ced0a414467a · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 22

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Observation c058db09-3ed3-4a96-b3a8-d2e9f125d406 · outbound

This paper cites Decoupled Weight Decay Regularization.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Decoupled Weight Decay Regularization

Reference 23

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Observation 66a79467-ecc7-4386-95a3-94f95d2199af · outbound

This paper cites Image segmenta- tion using text and image prompts.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Image segmenta- tion using text and image prompts

Reference 24

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Observation 423a7676-5230-4a98-853c-4404051b35dc · outbound

This paper cites Segment anything in medical images.Nature Communications, 15(1):654, 2024.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Segment anything in medical images.Nature Communications, 15(1):654, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T22:08:35.390143Z

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Observation bfdbcee2-dc86-4634-875b-f56a40ceac80 · outbound

This paper cites an unresolved cited work.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Unresolved cited work

Reference 26

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Observation bd5f5d94-28a6-464d-bdb0-1b417d1ebca3 · outbound

This paper cites Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using t1 mapping.Nature Medicine, 30(6):1749–1760, 2024.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using t1 mapping.Nature Medicine, 30(6):1749–1760, 2024

Reference 27

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Observation 348af945-f5e0-4029-96e0-8ed741b757bf · outbound

This paper cites Medical image segmentation methods, algorithms, and applications.IETE Technical Re- view, 31(3):199–213, 2014.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Medical image segmentation methods, algorithms, and applications.IETE Technical Re- view, 31(3):199–213, 2014

Reference 28

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Observation cd4ac85b-aa4f-49a4-8de2-2ea2f9adb6c9 · outbound

This paper cites Perceptiongpt: Effectively fusing visual perception into llm.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Perceptiongpt: Effectively fusing visual perception into llm

Reference 29

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Observation b4fedce9-daa5-4b74-bc17-f74b39772807 · outbound

This paper cites Reasoning to attend: Try to understand how¡ seg¿ token works.arXiv preprint arXiv:2412.17741, 2024.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Reasoning to attend: Try to understand how¡ seg¿ token works.arXiv preprint arXiv:2412.17741, 2024

Reference 30

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Observation ec14a06e-a1a3-4fc5-8fa2-55b5f545c6ef · outbound

This paper cites A review of medical image segmen- tation algorithms.EAI Endorsed Transactions on Pervasive Health & Technology, 7(27), 2021.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images A review of medical image segmen- tation algorithms.EAI Endorsed Transactions on Pervasive Health & Technology, 7(27), 2021

Reference 31

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Observation 488564f5-cf3a-46f2-996e-1dbb51e65240 · outbound

This paper cites Glamm: Pixel grounding large multimodal model.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Glamm: Pixel grounding large multimodal model

Reference 32

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Observation c1680b6c-adce-42a4-b3a3-47c210aa5ff8 · outbound

This paper cites Deepspeed: System optimizations enable train- ing deep learning models with over 100 billion parameters.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Deepspeed: System optimizations enable train- ing deep learning models with over 100 billion parameters

Reference 33

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Observation f3f023eb-a510-48fd-a734-4c37135512db · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images U- net: Convolutional networks for biomedical image segmen- tation

Reference 34

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Observation 8474063a-f145-4ea6-9299-a7380d010044 · outbound

This paper cites Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the vessel12 study.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the vessel12 study

Reference 35

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Observation aad99745-7553-448b-9936-e6e702ecf71c · outbound

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MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Unresolved cited work

Reference 36

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Observation 8044a0a2-df86-49b4-802a-10908bb9fc33 · outbound

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MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Unresolved cited work

Reference 37

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Observation d27f72b1-f41d-4f0e-9cb3-e809c2ba4d91 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Gemini: A Family of Highly Capable Multimodal Models

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Observation 5bfc90bc-c461-4bcb-81b2-be149898ce7b · outbound

This paper cites MediSee: Reasoning-based Pixel-level Perception in Medical Images.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images MediSee: Reasoning-based Pixel-level Perception in Medical Images

Reference 39

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Observation a147ea5e-3abf-4447-ae04-4d498000fe35 · outbound

This paper cites Llm-seg: Bridging image segmen- tation and large language model reasoning.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Llm-seg: Bridging image segmen- tation and large language model reasoning

Reference 40

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Observation a340c46c-32bb-453e-b1e9-a44195ae439c · outbound

This paper cites Medical image segmentation using deep learning: A survey.IET image processing, 16(5): 1243–1267, 2022.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Medical image segmentation using deep learning: A survey.IET image processing, 16(5): 1243–1267, 2022

Reference 41

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Observation 44e041a9-2fb9-493a-b007-602274b84637 · outbound

This paper cites SegLLM: Multi-round Reasoning Segmentation.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images SegLLM: Multi-round Reasoning Segmentation

Reference 42

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Observation 8c984d2d-a34f-400c-8131-319fad5906b8 · outbound

This paper cites Organ at risk segmentation in head and neck ct im- ages using a two-stage segmentation framework based on 3d u-net.IEEE Access, 7:144591–144602, 2019.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Organ at risk segmentation in head and neck ct im- ages using a two-stage segmentation framework based on 3d u-net.IEEE Access, 7:144591–144602, 2019

Reference 43

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Observation fff30cd6-168e-4378-9455-fe5f2237483b · outbound

This paper cites A learning algorithm for continually running fully recurrent neural networks.Neu- ral computation, 1(2):270–280, 1989.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images A learning algorithm for continually running fully recurrent neural networks.Neu- ral computation, 1(2):270–280, 1989

Reference 44

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Observation c87b3b95-93d7-43d2-9372-2bcdc0e01141 · outbound

This paper cites Deeplesion: automated mining of large-scale lesion annota- tions and universal lesion detection with deep learning.Jour- nal of medical imaging, 5(3):036501–036501, 2018.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Deeplesion: automated mining of large-scale lesion annota- tions and universal lesion detection with deep learning.Jour- nal of medical imaging, 5(3):036501–036501, 2018

Reference 45

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Observation 474cc81c-7832-460c-8b36-c599fa2e2516 · outbound

This paper cites Medreasoner: Reinforcement learning drives reasoning grounding from clinical thought to pixel-level precision.arXiv preprint arXiv:2508.08177,.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Medreasoner: Reinforcement learning drives reasoning grounding from clinical thought to pixel-level precision.arXiv preprint arXiv:2508.08177,

Reference 46

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Observation b8e6c691-003d-4ddb-9931-4eabec1e20c5 · outbound

This paper cites LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model

Reference 47

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Observation 89c26356-62ea-4ccd-af91-f6f6c4525371 · outbound

This paper cites SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 48

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Observation 0b5d19e3-4542-4384-b022-8553bc7d6b45 · outbound

This paper cites NExT-Chat: An LMM for Chat, Detection and Segmentation.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images NExT-Chat: An LMM for Chat, Detection and Segmentation

Reference 49

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Observation 8165ce3a-b575-4763-b19b-48f0c5338c3b · outbound

This paper cites A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities.Nature methods, pages 1–11,.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities.Nature methods, pages 1–11,

Reference 50

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Observation 1b806e41-47e5-4fe9-aa19-b7415f018973 · outbound

This paper cites Unet++: A nested u-net ar- chitecture for medical image segmentation.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images Unet++: A nested u-net ar- chitecture for medical image segmentation

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