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wentaozhu / DeepLung

WACV18 paper "DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification"
Apache License 2.0
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reproducing deeplung #64

Closed melody-rain closed 5 years ago

melody-rain commented 5 years ago

Could anyone reproduce the author's result?

I trained the model according to this repo with training data of luna subset 0~8 and test the model regarding luna subset 9 as the testing data.

By changing below to avoid GPU OOM:

margin = 16 # 32
sidelen = 144 # 144

my testing result is similar to this:

************************************************************
CAD Analysis: predanno-2d3
************************************************************

Candidate detection results:
    True positives: 69
    False positives: 1918
    False negatives: 1117
    True negatives: 0
    Total number of candidates: 2346
    Total number of nodules: 1186
    Ignored candidates on excluded nodules: 357
    Ignored candidates which were double detections on a nodule: 2
    Sensitivity: 0.058178752
    Average number of candidates per scan: 2.641891892

Even though I use the provided model to test,

res18fd0008.ckpt
res18fd1049.ckpt
res18fd2020.ckpt
res18fd3006.ckpt
res18fd4143.ckpt
res18fd5097.ckpt
res18fd6028.ckpt
res18fd7024.ckpt
res18fd8014.ckpt
res18fd9020.ckpt

I just got poor sensitivity which is less than 0.1.

So have anyone reproduced the paper? Thanks.

wentaozhu commented 5 years ago

Many researchers have reproduced the work. You may read the previous issues to solve the OOM. If you have results, please reply here so that we can help ours. Thanks!

melody-rain commented 5 years ago

@wentaozhu

I forgot to combine the detection csvs into one. After combining, I got similar results to your paper.

Thanks.

Leebanana commented 5 years ago

Do you got get 1-fold performance by concatenating the result csvfiles which are in the 150 val?Or you concatenated the 10 csvfiles which are the results of all 10-fold in every val150 file to get the fina performance?

wentaozhu commented 5 years ago

later one