Forithmus/mrrate_temp_1
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published
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Forithmus/mrrate_temp_1
updated
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6 days ago
Forithmus/MR-RATE
published
a dataset
9 days ago
Forithmus/MR-RATE
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updated
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authored
12
papers
21 days ago
DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays
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2305.19112
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Published
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3
GenerateCT: Text-Guided 3D Chest CT Generation
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2305.16037
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Published
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3
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
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2303.06500
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Published
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3
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
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2103.01006
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Published
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1
A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities
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2403.17834
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Published
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4
CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging
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2403.06801
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Published
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1
Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering
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2505.18915
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Published
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
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2507.22953
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Published
Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks
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2510.14803
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Published
Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging
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2510.20639
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Published
RadDiagSeg-M: A Vision Language Model for Joint Diagnosis and Multi-Target Segmentation in Radiology
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2510.18188
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Published
See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement
Paper
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2512.07251
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Published
updated
a
dataset
about 1 month ago
published
a
dataset
about 1 month ago
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Post
3743
From Prompt Engineering to Context Engineering: Main Design Patterns
Earlier on, we relied on clever prompt wording, but now structured, complete context matters more than just magic phrasing. The next year is going to be a year of context engineering which expands beyond prompt engineering. The two complement each other: prompt engineering shapes how we ask, while context engineering shapes what the model knows, sees, and can do.
To keep things clear, here are the main techniques and design patterns in both areas, with some useful resources for further exploration:
▪️ 9 Prompt Engineering Techniques (configuring input text)
1. Zero-shot prompting – giving a single instruction without examples. Relies entirely on pretrained knowledge.
2. Few-shot prompting – adding input–output examples to encourage model to show the desired behavior. ⟶ https://arxiv.org/abs/2005.14165
3. Role prompting – assigning a persona or role (e.g. "You are a senior researcher," "Say it as a specialist in healthcare") to shape style and reasoning. ⟶ https://arxiv.org/abs/2403.02756
4. Instruction-based prompting – explicit constraints or guidance, like "think step by step," "use bullet points," "answer in 10 words"
5. Chain-of-Thought (CoT) – encouraging intermediate reasoning traces to improve multi-step reasoning. It can be explicit ("let’s think step by step"), or implicit (demonstrated via examples). ⟶ https://arxiv.org/abs/2201.11903
6. Tree-of-Thought (ToT) – the model explores multiple reasoning paths in parallel, like branches of a tree, instead of following a single chain of thought. ⟶ https://arxiv.org/pdf/2203.11171
7. Reasoning–action prompting (ReAct-style) – prompting the model to interleave reasoning steps with explicit actions and observations. It defines action slots and lets the model generate a sequence of "Thought → Action → Observation" steps. ⟶ https://arxiv.org/abs/2210.03629
Read further ⬇️
Also subscribe to Turing Post: https://www.turingpost.com/subscribe
Earlier on, we relied on clever prompt wording, but now structured, complete context matters more than just magic phrasing. The next year is going to be a year of context engineering which expands beyond prompt engineering. The two complement each other: prompt engineering shapes how we ask, while context engineering shapes what the model knows, sees, and can do.
To keep things clear, here are the main techniques and design patterns in both areas, with some useful resources for further exploration:
▪️ 9 Prompt Engineering Techniques (configuring input text)
1. Zero-shot prompting – giving a single instruction without examples. Relies entirely on pretrained knowledge.
2. Few-shot prompting – adding input–output examples to encourage model to show the desired behavior. ⟶ https://arxiv.org/abs/2005.14165
3. Role prompting – assigning a persona or role (e.g. "You are a senior researcher," "Say it as a specialist in healthcare") to shape style and reasoning. ⟶ https://arxiv.org/abs/2403.02756
4. Instruction-based prompting – explicit constraints or guidance, like "think step by step," "use bullet points," "answer in 10 words"
5. Chain-of-Thought (CoT) – encouraging intermediate reasoning traces to improve multi-step reasoning. It can be explicit ("let’s think step by step"), or implicit (demonstrated via examples). ⟶ https://arxiv.org/abs/2201.11903
6. Tree-of-Thought (ToT) – the model explores multiple reasoning paths in parallel, like branches of a tree, instead of following a single chain of thought. ⟶ https://arxiv.org/pdf/2203.11171
7. Reasoning–action prompting (ReAct-style) – prompting the model to interleave reasoning steps with explicit actions and observations. It defines action slots and lets the model generate a sequence of "Thought → Action → Observation" steps. ⟶ https://arxiv.org/abs/2210.03629
Read further ⬇️
Also subscribe to Turing Post: https://www.turingpost.com/subscribe
- 3 replies
updated
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dataset
2 months ago
Z-spacing contraditory info
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#104 opened 3 months ago
by
anamsousa