error while loading shared libraries: libmpfr.so.6: cannot open shared object file: No such file or directory 安装 libmpfr6-4.1.0-alt1.x86_64.rpm 并且将缺少的库放到 /usr/lib64 中。
The goal of this competition is to create models that can be used to aid in the detection and classification of degenerative spine conditions using lumbar spine MR images. Competitors will develop models that simulate a radiologist’s performance in diagnosing spine conditions.
Low back pain is the leading cause of disability worldwide, according to the World Health Organization, affecting 619 million people in 2020. Most people experience low back pain at some point in their lives, with the frequency increasing with age. Pain and restricted mobility are often symptoms of spondylosis, a set of degenerative spine conditions including degeneration of intervertebral discs and subsequent narrowing of the spinal canal (spinal stenosis), subarticular recesses, or neural foramen with associated compression or irritations of the nerves in the low back.
Magnetic resonance imaging (MRI) provides a detailed view of the lumbar spine vertebra, discs and nerves, enabling radiologists to assess the presence and severity of these conditions. Proper diagnosis and grading of these conditions help guide treatment and potential surgery to help alleviate back pain and improve overall health and quality of life for patients.
RSNA has teamed with the American Society of Neuroradiology (ASNR) to conduct this competition exploring whether artificial intelligence can be used to aid in the detection and classification of degenerative spine conditions using lumbar spine MR images.
The challenge will focus on the classification of five lumbar spine degenerative conditions: Left Neural Foraminal Narrowing, Right Neural Foraminal Narrowing, Left Subarticular Stenosis, Right Subarticular Stenosis, and Spinal Canal Stenosis. For each imaging study in the dataset, we’ve provided severity scores (Normal/Mild, Moderate, or Severe) for each of the five conditions across the intervertebral disc levels L1/L2, L2/L3, L3/L4, L4/L5, and L5/S1.
To create the ground truth dataset, the RSNA challenge planning task force collected imaging data sourced from eight sites on five continents. This multi-institutional, expertly curated dataset promises to improve standardized classification of degenerative lumbar spine conditions and enable development of tools to automate accurate and rapid disease classification.
Challenge winners will be recognized at an event during the RSNA 2024 annual meeting. For more information on the challenge, contact RSNA Informatics staff at informatics@rsna.org.
Submissions are evaluated using the average of sample weighted log losses and an any_severe_spinal prediction generated by the metric. The metric notebook can be found here.
For each row ID in the test set, you must predict a probability for each of the different severity levels. The file should contain a header and have the following format:
对于测试集中的每个行 ID，您必须预测每个不同严重性级别的概率。该文件应包含标头并具有以下格式：
1 2 3 4 5
row_id,normal_mild,moderate,severe 123456_left_neural_foraminal_narrowing_l1_l2,0.333,0.333,0.333 123456_left_neural_foraminal_narrowing_l2_l3,0.333,0.333,0.333 123456_left_neural_foraminal_narrowing_l3_l4,0.333,0.333,0.333 etc.
row_id
normal_mild
moderate
severe
123456_left_neural_foraminal_narrowing_l1_l2
0.333
0.333
0.333
123456_left_neural_foraminal_narrowing_l2_l3
0.333
0.333
0.333
123456_left_neural_foraminal_narrowing_l3_l4
0.333
0.333
0.333
etc.
In rare cases the lowest vertebrae aren’t visible in the imagery. You still need to make predictions (nulls will cause errors), but those rows will not be scored.
在极少数情况下，图像中看不到最低的椎骨。您仍然需要进行预测（空值会导致错误），但这些行不会被评分。
For this competition, theany_severe_scalarhas been set to1.0.
对于本次比赛，any_severe_scalar已设置为1.0 。
Dataset Description 数据集描述
The goal of this competition is to identify medical conditions affecting the lumbar spine in MRI scans. 本次比赛的目标是通过 MRI 扫描识别影响腰椎的医疗状况。
This competition uses a hidden test. When your submitted notebook is scored, the actual test data (including a full length sample submission) will be made available to your notebook. 本次比赛采用隐藏测试方式。当您提交的笔记本电脑被评分时，实际的测试数据（包括完整长度的样本提交）将提供给您的笔记本电脑。
Files 文件
train.csvLabels for the train set. 训练集的标签。
study_id - The study ID. Each study may include multiple series of images. study_id - 研究 ID，每个研究可能包括多个系列的图像。
[condition]_[level] - The target labels, such asspinal_canal_stenosis_l1_l2, with the severity levels ofNormal/Mild,Moderate, orSevere. Some entries have incomplete labels. [condition]_[level] - 目标标签，例如spinal_canal_stenosis_l1_l2 ，严重程度级别为 Normal/Mild 、 Moderate 或 Severe。有些条目的标签不完整。
train_label_coordinates.csv 训练标签坐标.csv
study_id
series_id - The imagery series ID. series_id - 图像系列 ID。
instance_number - The image’s order number within the 3D stack. instance_number - 图像在 3D 堆栈中的顺序号。
condition - There are three core conditions: spinal canal stenosis, neural_foraminal_narrowing, and subarticular_stenosis. The latter two are considered for each side of the spine. condition - 共有三种核心病症：椎管狭窄、神经椎间孔狭窄和关节下狭窄。脊柱的每一侧都考虑后两者。
level - The relevant vertebrae, such as l3_l4 level - 相关椎骨，例如l3_l4
[x/y] - The x/y coordinates for the center of the area that defined the label. [x/y] - 定义标签的区域中心的 x/y 坐标。
sample_submission.csv 样本提交.csv
row_id - A slug of the study ID, condition, and level such as 12345_spinal_canal_stenosis_l3_l4. row_id - 研究 ID、条件和级别的 slug，例如12345_spinal_canal_stenosis_l3_l4 。
[normal_mild/moderate/severe] - The three prediction columns. [normal_mild/moderate/severe] - 三个预测列。
[train/test]_images/[study_id]/[series_id]/[instance_number].dcm The imagery data.图像数据。
[train/test]_series_descriptions.csv
study_id
series_id
series_description The scan’s orientation. series_description 扫描方向。
completion = client.chat.completions.create( model="qwen", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "tell me something about michael jordan"} ] ) print(completion.choices[0].message.content)
下载好响应的模型后，将模型组织好，放到相应的文件夹中，这里按照 models/Publisher/Repository/*.gguf 的路径组织模型路径，然后选择 Change 更改模型的位置。如果不按照该路径组织，则会出现 You have 1 uncategorized model files. 错误，如下图所示：
# Replace '1100' to your card architecture name, you can get it by rocminfo CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake -S . -B build - DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1100 cmake --build build --config Release
